https://www2.icp.uni-stuttgart.de/~icp/mediawiki/api.php?action=feedcontributions&user=Stovey&feedformat=atomICPWiki - User contributions [en]2022-08-18T14:18:53ZUser contributionsMediaWiki 1.35.7https://www2.icp.uni-stuttgart.de/~icp/mediawiki/index.php?title=Advanced_Simulation_Methods_SS_2022&diff=26105Advanced Simulation Methods SS 20222022-04-10T20:41:42Z<p>Stovey: /* Module 1: Maria Fyta and Samuel Tovey: Machine-learned Interatomic Potentials */</p>
<hr />
<div>== Overview ==<br />
<br />
;Type<br />
:Lecture and Tutorials (2 SWS in total)<br />
;Lecturer<br />
:Prof. Dr. [[Christian Holm]], PD. Dr. [[Jens Smiatek]], aplProf. Dr. [[Maria Fyta]]<br />
;Course language<br />
:English or German<br />
;Location<br />
:ICP, Allmandring 3; Room: ICP Meeting Room<br />
;Time<br />
:(see below)<br />
The course will consist of three modules supervised by Prof. Dr. [[Christian Holm]], PD. Dr. [[Jens Smiatek]], and aplProf. Dr. [[Maria Fyta]]. It will contain exercises, presentations, discussion meetings, and written reports, worked out in groups. Each group will have to give a talk for all modules.<br />
The students can work in groups. All groups should write a report of about 10 pages on each module, which they should submit to the responsible person for each module by the deadline set for each module.<br />
<br />
<br />
<br />
== Module 1: [[Maria Fyta]] and [[Samuel Tovey]]: Machine-learned Interatomic Potentials ==<br />
<br />
=== Dates ===<br />
<br />
First meeting: Friday, April 23, 2021 at 10:00 (online or in person TBA) <!-- Friday, April 12 at 11:30 --> in the ICP meeting room (Allmandring 3, 1st floor, room 1.095).<br />
<br />
Final meeting and presentation: Friday, May 22; time tba <!--Friday, May 10 at 11:30 --> in the ICP meeting room (Allmandring 3, 1st floor, room 1.095).<br />
<br />
Tutorials: Fridays 11:30-13:00 in the ICP CIP-Pool. The first tutorial will take place on tba <!-- Wed. April 17 at 15:00-16:30.--><br />
<br />
Deadline for reports: tba--><br />
<br />
=== Description ===<br />
<br />
This exercise introduces student to the process of developing an inter-atomic potential for liquid argon using machine learning methods. You will follow the process from start to finish, using ab-initio MD methods to construct training data before fitting a model and deploying it in scaled up simulations.<br />
<br />
=== Contact ===<br />
<br />
If you have any questions regarding the organization or content of this module please do not hesitate to contact [[Christian Holm]]. For practical guidance regarding the simulations [[Samuel Tovey]].<br />
<br />
=== Part 1 -- DFT Simulations ===<br />
<br />
In the first part of the exercise, you will use the CP2K simulation software to perform ab-initio molecular dynamics simulations on a system of liquid argon, in the process experimenting with configuring the interactions between atoms and seeing the results.<br />
<br />
=== Part 2 -- Fitting a Potential ===<br />
<br />
<br />
In this part, students will use the data generated in part 1 to fit a Gaussian process regression based machine learned inter-atomic potential. This task will allow the students to develop a deeper understanding of how these potentials are fit and the different parameters that need to be optimised in the process.<br />
<br />
=== Part 3 ===<br />
<br />
In part 3, students use the machine-learned potential to perform scaled up molecular dynamics simulations using the LAMMPS simulation engine. These simulations are compared to the ab-initio data to demonstrate the retention of accuracy with the significantly improved performance.<br />
<br />
==== Tutorial ====<br />
<br />
==== Literature ====<br />
<br />
* Bartók, A. P., Payne, M. C., Kondor, R. & Gábor, C. Gaussian approximation potentials: the accuracy of quantum mechanics, without the electrons. Phys. Rev. Lett. 104, 136403 (2010).<br />
<br />
==== Further reading (if interested) ====<br />
<br />
* ...<br />
<br />
== Module 2: [[Jens Smiatek]]: Molecular Theories of Solutions ==<br />
<br />
<br />
=== Dates ===<br />
<br />
First meeting: tba<br />
<br />
Final meeting and presentation: tba.<br />
<br />
Tutorials: tba.<br />
<br />
Deadline for reports: tba<br />
<br />
=== Description ===<br />
<br />
This module focuses on molecular theories of solution. I will outline the main principles of solvation processes and the corresponding interactions. As we will see, most mechanisms strongly differ from highly idealized assumptions such that more effective descriptions are needed. Such considerations are represented by the Kirkwood-Buff theory of solutions or in the framework of conceptual density functional theory. The corresponding theories will be applied for the study of water-ionic liquid mixtures.<br />
<br />
=== Lecture Notes ===<br />
[[Media:Molecular.pdf|Lecture Notes for Part: Molecular Theories of Solution]]<br />
<br />
[[Media:conceptual_DFT.pdf|Lecture Notes for Part: Conceptual Density Functional Theory]]<br />
<br />
=== Contact ===<br />
<br />
If you have any questions regarding the organization or content of this module, please do not hesitate to contact ???.<br />
<br />
=== Part 1: Molecular Theories of Solution ===<br />
<br />
==== Description ====<br />
<br />
This part introduces the students to the field of solution research. An important theory to study solvation and binding behavior is given by the Kirkwood-Buff theory which can be well applied to computer simulations.<br />
The students should study the literature given below and present their findings. The presentation should at a minimum contain an introduction to Kirkwood-Buff theory in the context of the simulations.<br />
<br />
==== Literature ====<br />
<br />
* J. G. Kirkwood and F. P. Buff. "The statistical mechanical theory of solutions. I." J. Chem. Phys. 19, 774 (1951) <br />
* V. Pierce, M. Kang, M. Aburi, S. Weerasinghe and P. E. Smith, "Recent applications of Kirkwood–Buff theory to biological systems", Cell Biochem. Biophys. 50, 1 (2008)<br />
* J. Rösgen, B. M. Pettitt and D. W. Bolen, "Protein folding, stability, and solvation structure in osmolyte solutions", Biophys. J. 89, 2988 (2005)<br />
* J. Smiatek, "Aqueous ionic liquids and their effects on protein structures: an overview on recent theoretical and experimental results", J. Phys. Condens. Matter 29, 233001 (2017)<br />
* E. A. Oprzeska-Zingrebe and J. Smiatek, "Aqueous ionic liquids in comparison with standard co-solutes - Differences and common principles in their interaction with protein and DNA structures", Biophys. Rev. 10, 809 (2018)<br />
* J. Smiatek, A. Heuer and M. Winter, "Properties of ion complexes and their impact on charge transport in organic solvent-based electrolyte solutions for lithium batteries: insights from a theoretical perspective", Batteries 4, 62 (2018)<br />
* T. Kobayashi <i>et al</i>, "The properties of residual water molecules in ionic liquids: a comparison between direct and inverse Kirkwood–Buff approaches", Phys. Chem. Chem. Phys. 19, 18924 (2017)<br />
<br />
=== Part 2: Simulations ===<br />
<br />
==== Description ====<br />
<br />
This part is practical. The simulations will be conducted by the software package GROMACS [http://www.gromacs.org/]. The students will perform the simulations of ionic liquids(IL)-water mixtures at different water concentration in combination with the SPC/E water model and OPLSAA force field for EMImBF4. To generate the initial configuration of the simulation boxes, the software package Packmol [http://www.ime.unicamp.br/~martinez/packmol] will be used.<br />
<br />
First the students simulate pure water and pure IL, and analyze the output data. Following properties will be calculated. The Kirkwood-Buff theory will be used to calculate the Kirkwood-Buff integrals. The student perform the different simulation box size to estimate the proper box size for calculating the properties.<br />
* Kirkwood-Buff integrals<br />
* diffusion coefficients<br />
* mass densities<br />
In addition to above, for water<br />
* hydrogen bond life times and number of hydrogen bonds for water-water pairs<br />
* water mean relaxation times<br />
<br />
Next the student perform the IL-water mixtures at different water concentrations. After energy minimization and warm up, run 500 ns simulations with GROMACS for water mole fractions between X_H2O = 0 - 0.30.<br />
<br />
In comparison to pure water/pure IL, the students will analyze several properties stated above and elucidate their water concentration dependent behavior.<br />
Interpret the corresponding results with regard to the findings in Phys.Chem.Chem.Phys. 19, 18924 (2017). <br />
<br />
All the data needed for the exercise can be found in /group/sm/2019/Advsm_part2 <br />
<br />
<!--<br />
==== Force Fields for IL(EMImBF4) ====<br />
<br />
* {{Download| hectoinzwittmp2.itp |itp-File for Hydroxyectoine}}<br />
* {{Download| hectoinzwittmp2.gro |gro-File for Hydroxyectoine}}<br />
* {{Download| ectoinzwittmp2.itp |itp-File for Ectoine}}<br />
* {{Download| ectoinzwittmp2.gro |gro-File for Ectoine}}<br />
--><br />
<br />
<!--1. Implement the developed force fields for the osmolytes (urea, ectoine and hydroxyectoine) in combination with the SPC/E water model. After energy minimization and warm up, run 20-30 ns simulations with GROMACS for osmolyte concentrations between c = 0 - 6 M.<br />
<br />
2. Study the following properties for the different osmolytes and concentrations:<br />
* diffusion coefficients<br />
* hydrogen bond life times and number of hydrogen bonds for water-water, water-osmolyte and osmolyte-osmolyte pairs<br />
* water mean relaxation times<br />
Interpret the corresponding results. Are the molecules kosmotropes or chaotropes?<br />
<br />
3. Calculate the radial distribution functions for all systems in terms of water-water, water-osmolyte and osmolyte-osmolyte pairs.<br />
Use this information to compute the<br />
<br />
* derivatives of the chemical activity<br />
* derivatives of the activity coefficient<br />
Interpret the corresponding results with regard to the findings in Biochemistry 43, 14472 (2004). <br />
<br />
==== Literature ====<br />
<br />
* D. van der Spoel, P. J. van Maaren, P. Larsson and N. Timneanu, "Thermodynamics of hydrogen bonding in hydrophilic and hydrophobic media", J. Phys. Chem. B 110, 4393 (2006)<br />
* J. Wang, R. M. Wolf, J. W. Caldwell, P. A. Kollman and D. A. Case, "Development and testing of a general Amber force field", J. Comp. Chem. 25, 1157 (2004)<br />
--><br />
<br />
== Module 3: [[Christian Holm]], [[Alexander Reinauer]]: Electrostatics, Lattice Boltzmann, and Electrokinetics==<br />
<br />
<br />
=== Dates ===<br />
<br />
First meeting: tba<br />
<br />
Final meeting and presentation:tba in the ICP meeting room (Allmandring 3, 1st floor, room 1.095).<br />
<br />
Tutorials: tba in the ICP CIP-Pool.<br />
<br />
Deadline for reports: tba<br />
<br />
=== Description ===<br />
<br />
This module focuses on charged matter with electrostatic and hydrodynamic interactions. It should be taken in groups of three people.<br />
It consists of one lecture on electrostatic algorithms, simulations, theory, a presentation and a short report on the simulation results. You only have to give one common presentation<br />
and hand in one report. The Module 3 consists of three parts.<br />
<br />
=== Contact ===<br />
If you have any questions regarding the organisation or content of this module please do not hesitate to contact [[Christian Holm]].<br />
For questions regarding the practical part of the module and technical help contact [[Alexander Reinauer]].<br />
<br />
=== Part 1: Electrostatics ===<br />
==== Description ====<br />
This part is about the theory of electrostatic algorithms for molecular dynamics simulations.<br />
It is concerned with state of the art algorithms beyond the Ewald sum, especially mesh Ewald<br />
methods. To this end the students should read the referenced literature. [[Christian Holm]] will give an hour long lecture. Afterwards we will discuss the content and try to resolve open questions. The presentation should foster the students understanding of the P3M method as well<br />
as give them an overview of its performance compared to other modern electrostatics methods.<br />
<br />
==== Literature ====<br />
:* C. Holm.<br />'''"Simulating Long range interactions".'''<br />''Institute for Computational Physics, Universitat Stuttgart,'' '''2018'''. <br /> [[Media:longrange.pdf|[PDF]]] (15.4 MB) <br /><br />
<bibentry>deserno98a,arnold13b,arnold05a</bibentry><br />
<br />
=== Part 2: Electro-Osmotic Flow ===<br />
==== Description ====<br />
[[File:Slitpore.png|550px|right|Electroosmotic flow in a slit pore]]<br />
This part is practical. It is concerned with the movement of ions in an charged slit pore.<br />
It is similar to the systems that are discussed in the Bachelors thesis of [[Georg Rempfer]]<br />
which is recommended reading. A slit pore consists of two infinite charge walls as shown<br />
in the figure to the right. In this exercise you should simulate such a system with [http://espressomd.org ESPResSo].<br />
You are supposed to use a Lattice Boltzmann fluid coupled to explicit ions which are represented<br />
by charge Week-Chandler-Anderson spheres.<br />
In addition to the charge on the walls, the ions are also subject to an external electrical field parallel to the walls.<br />
Electrostatics should be handled by the P3M algorithm with ELC.<br />
A set of realistic parameters and an more in detail description of the system can be found in the<br />
thesis.<br />
You should measure the flow profile of the fluid and the density and velocity profiles of the ions. The case of the slit<br />
pore can be solved analytically either in the case of only counter ions (the so called salt free case) or in the high<br />
salt limit (Debye-Hueckel-Limit).<br />
Calculate the ion profiles in one or both of these cases and compare the results with the simulation.<br />
<br />
===== Worksheet =====<br />
<br />
Worksheet 2021 {{Download|SS21_adv_sm_mod3_part2.pdf|Detailed worksheet}}<br />
<br />
==== Literature ====<br />
<br />
Some ESPResSo tutorials can be helpful.<br />
* Introductory tutorials, Intermediate tutorials: Lattice-Boltzmann and Charged systems [https://espressomd.github.io/tutorials4.1.4.html Tutorials for ESPResSo 4.1.4] <br />
* The [https://espressomd.github.io/tutorials4.1.4/02-charged_system/02-charged_system-2.html Part 2 of the charged systems tutorial] to see how to setup proper electrostatics in quasi-2D geometry.<br />
<br />
* Georg Rempfer, {{Download|BSc_thesis_rempfer.pdf|"Lattice-Boltzmann Simulations in Complex Geometries"}}, 2010, Institute for Computational Physics, Stuttgart<br />
<br />
=== Part 3: Electrophoresis of Polyelectrolytes ===<br />
==== Description ====<br />
In this part you simulate the movement of a charged polymer under the influence of an external electrical field and hydrodynamic interactions.<br />
Set up a system consisting of a charged polymer, ions with the opposite charge to make the system neutral and an Lattice Boltzmann fluid coupled with <br />
the the ions and polymer. Apply an external field and measure the center of mass velocity of the polymer as a function of the length of the polymer<br />
for polymers of one to 20 monomers. Make sure the system is in equilibrium before you start the sampling. Compare your result to theory and<br />
experimental results (see literature).<br />
<br />
<br />
==== Worksheet ====<br />
{{Download|SS21_adv_sm_mod3_part3.pdf|Detailed worksheet}}<br />
<br />
==== Instructions and Literature ====<br />
General part and part 5 of [[Media:04-lattice_boltzmann.pdf]]<br />
<br />
<bibentry>grass08a, grass09c</bibentry><br />
<br />
=== Report ===<br />
<br />
At the final meeting day of this module, one group will give a presentation about the learned and performed work. In addition, they write a report of about 5 pages containing and discussing the obtained results and hand it in together with the reports of the other modules at the end of the course (see above).<br />
<br />
The final report is due electronically TBA<br />
<br />
<br />
<br />
<!--Please write together one report of 5 to 10 pages containing and discussing your simulation results from part 2 and 3.--></div>Stoveyhttps://www2.icp.uni-stuttgart.de/~icp/mediawiki/index.php?title=Advanced_Simulation_Methods_SS_2022&diff=26104Advanced Simulation Methods SS 20222022-04-10T20:38:29Z<p>Stovey: /* Description */</p>
<hr />
<div>== Overview ==<br />
<br />
;Type<br />
:Lecture and Tutorials (2 SWS in total)<br />
;Lecturer<br />
:Prof. Dr. [[Christian Holm]], PD. Dr. [[Jens Smiatek]], aplProf. Dr. [[Maria Fyta]]<br />
;Course language<br />
:English or German<br />
;Location<br />
:ICP, Allmandring 3; Room: ICP Meeting Room<br />
;Time<br />
:(see below)<br />
The course will consist of three modules supervised by Prof. Dr. [[Christian Holm]], PD. Dr. [[Jens Smiatek]], and aplProf. Dr. [[Maria Fyta]]. It will contain exercises, presentations, discussion meetings, and written reports, worked out in groups. Each group will have to give a talk for all modules.<br />
The students can work in groups. All groups should write a report of about 10 pages on each module, which they should submit to the responsible person for each module by the deadline set for each module.<br />
<br />
<br />
<br />
== Module 1: [[Maria Fyta]] and [[Samuel Tovey]]: Machine-learned Interatomic Potentials ==<br />
<br />
=== Dates ===<br />
<br />
First meeting: Friday, April 23, 2021 at 10:00 (online or in person TBA) <!-- Friday, April 12 at 11:30 --> in the ICP meeting room (Allmandring 3, 1st floor, room 1.095).<br />
<br />
Final meeting and presentation: Friday, May 22; time tba <!--Friday, May 10 at 11:30 --> in the ICP meeting room (Allmandring 3, 1st floor, room 1.095).<br />
<br />
Tutorials: Fridays 11:30-13:00 in the ICP CIP-Pool. The first tutorial will take place on tba <!-- Wed. April 17 at 15:00-16:30.--><br />
<br />
Deadline for reports: tba--><br />
<br />
=== Description ===<br />
<br />
This exercise introduces student to the process of developing an inter-atomic potential for liquid argon using machine learning methods. You will follow the process from start to finish, using ab-initio MD methods to construct training data before fitting a model and deploying it in scaled up simulations.<br />
<br />
=== Contact ===<br />
<br />
If you have any questions regarding the organization or content of this module please do not hesitate to contact [[Christian Holm]]. For practical guidance regarding the simulations [[Samuel Tovey]].<br />
<br />
=== CP2K ===<br />
<br />
==== Description ====<br />
<br />
=== GAPs ===<br />
<br />
==== Description ====<br />
<br />
This exercise introduces student to the process of developing an inter-atomic potential for liquid argon using machine learning methods. You will follow the process from start to finish, using ab-initio MD methods to construct training data before fitting a model and deploying it in scaled up simulations.<br />
<br />
==== Tutorial ====<br />
<br />
==== Literature ====<br />
<br />
* Bartók, A. P., Payne, M. C., Kondor, R. & Gábor, C. Gaussian approximation potentials: the accuracy of quantum mechanics, without the electrons. Phys. Rev. Lett. 104, 136403 (2010).<br />
<br />
==== Further reading (if interested) ====<br />
<br />
* ...<br />
<br />
== Module 2: [[Jens Smiatek]]: Molecular Theories of Solutions ==<br />
<br />
<br />
=== Dates ===<br />
<br />
First meeting: tba<br />
<br />
Final meeting and presentation: tba.<br />
<br />
Tutorials: tba.<br />
<br />
Deadline for reports: tba<br />
<br />
=== Description ===<br />
<br />
This module focuses on molecular theories of solution. I will outline the main principles of solvation processes and the corresponding interactions. As we will see, most mechanisms strongly differ from highly idealized assumptions such that more effective descriptions are needed. Such considerations are represented by the Kirkwood-Buff theory of solutions or in the framework of conceptual density functional theory. The corresponding theories will be applied for the study of water-ionic liquid mixtures.<br />
<br />
=== Lecture Notes ===<br />
[[Media:Molecular.pdf|Lecture Notes for Part: Molecular Theories of Solution]]<br />
<br />
[[Media:conceptual_DFT.pdf|Lecture Notes for Part: Conceptual Density Functional Theory]]<br />
<br />
=== Contact ===<br />
<br />
If you have any questions regarding the organization or content of this module, please do not hesitate to contact ???.<br />
<br />
=== Part 1: Molecular Theories of Solution ===<br />
<br />
==== Description ====<br />
<br />
This part introduces the students to the field of solution research. An important theory to study solvation and binding behavior is given by the Kirkwood-Buff theory which can be well applied to computer simulations.<br />
The students should study the literature given below and present their findings. The presentation should at a minimum contain an introduction to Kirkwood-Buff theory in the context of the simulations.<br />
<br />
==== Literature ====<br />
<br />
* J. G. Kirkwood and F. P. Buff. "The statistical mechanical theory of solutions. I." J. Chem. Phys. 19, 774 (1951) <br />
* V. Pierce, M. Kang, M. Aburi, S. Weerasinghe and P. E. Smith, "Recent applications of Kirkwood–Buff theory to biological systems", Cell Biochem. Biophys. 50, 1 (2008)<br />
* J. Rösgen, B. M. Pettitt and D. W. Bolen, "Protein folding, stability, and solvation structure in osmolyte solutions", Biophys. J. 89, 2988 (2005)<br />
* J. Smiatek, "Aqueous ionic liquids and their effects on protein structures: an overview on recent theoretical and experimental results", J. Phys. Condens. Matter 29, 233001 (2017)<br />
* E. A. Oprzeska-Zingrebe and J. Smiatek, "Aqueous ionic liquids in comparison with standard co-solutes - Differences and common principles in their interaction with protein and DNA structures", Biophys. Rev. 10, 809 (2018)<br />
* J. Smiatek, A. Heuer and M. Winter, "Properties of ion complexes and their impact on charge transport in organic solvent-based electrolyte solutions for lithium batteries: insights from a theoretical perspective", Batteries 4, 62 (2018)<br />
* T. Kobayashi <i>et al</i>, "The properties of residual water molecules in ionic liquids: a comparison between direct and inverse Kirkwood–Buff approaches", Phys. Chem. Chem. Phys. 19, 18924 (2017)<br />
<br />
=== Part 2: Simulations ===<br />
<br />
==== Description ====<br />
<br />
This part is practical. The simulations will be conducted by the software package GROMACS [http://www.gromacs.org/]. The students will perform the simulations of ionic liquids(IL)-water mixtures at different water concentration in combination with the SPC/E water model and OPLSAA force field for EMImBF4. To generate the initial configuration of the simulation boxes, the software package Packmol [http://www.ime.unicamp.br/~martinez/packmol] will be used.<br />
<br />
First the students simulate pure water and pure IL, and analyze the output data. Following properties will be calculated. The Kirkwood-Buff theory will be used to calculate the Kirkwood-Buff integrals. The student perform the different simulation box size to estimate the proper box size for calculating the properties.<br />
* Kirkwood-Buff integrals<br />
* diffusion coefficients<br />
* mass densities<br />
In addition to above, for water<br />
* hydrogen bond life times and number of hydrogen bonds for water-water pairs<br />
* water mean relaxation times<br />
<br />
Next the student perform the IL-water mixtures at different water concentrations. After energy minimization and warm up, run 500 ns simulations with GROMACS for water mole fractions between X_H2O = 0 - 0.30.<br />
<br />
In comparison to pure water/pure IL, the students will analyze several properties stated above and elucidate their water concentration dependent behavior.<br />
Interpret the corresponding results with regard to the findings in Phys.Chem.Chem.Phys. 19, 18924 (2017). <br />
<br />
All the data needed for the exercise can be found in /group/sm/2019/Advsm_part2 <br />
<br />
<!--<br />
==== Force Fields for IL(EMImBF4) ====<br />
<br />
* {{Download| hectoinzwittmp2.itp |itp-File for Hydroxyectoine}}<br />
* {{Download| hectoinzwittmp2.gro |gro-File for Hydroxyectoine}}<br />
* {{Download| ectoinzwittmp2.itp |itp-File for Ectoine}}<br />
* {{Download| ectoinzwittmp2.gro |gro-File for Ectoine}}<br />
--><br />
<br />
<!--1. Implement the developed force fields for the osmolytes (urea, ectoine and hydroxyectoine) in combination with the SPC/E water model. After energy minimization and warm up, run 20-30 ns simulations with GROMACS for osmolyte concentrations between c = 0 - 6 M.<br />
<br />
2. Study the following properties for the different osmolytes and concentrations:<br />
* diffusion coefficients<br />
* hydrogen bond life times and number of hydrogen bonds for water-water, water-osmolyte and osmolyte-osmolyte pairs<br />
* water mean relaxation times<br />
Interpret the corresponding results. Are the molecules kosmotropes or chaotropes?<br />
<br />
3. Calculate the radial distribution functions for all systems in terms of water-water, water-osmolyte and osmolyte-osmolyte pairs.<br />
Use this information to compute the<br />
<br />
* derivatives of the chemical activity<br />
* derivatives of the activity coefficient<br />
Interpret the corresponding results with regard to the findings in Biochemistry 43, 14472 (2004). <br />
<br />
==== Literature ====<br />
<br />
* D. van der Spoel, P. J. van Maaren, P. Larsson and N. Timneanu, "Thermodynamics of hydrogen bonding in hydrophilic and hydrophobic media", J. Phys. Chem. B 110, 4393 (2006)<br />
* J. Wang, R. M. Wolf, J. W. Caldwell, P. A. Kollman and D. A. Case, "Development and testing of a general Amber force field", J. Comp. Chem. 25, 1157 (2004)<br />
--><br />
<br />
== Module 3: [[Christian Holm]], [[Alexander Reinauer]]: Electrostatics, Lattice Boltzmann, and Electrokinetics==<br />
<br />
<br />
=== Dates ===<br />
<br />
First meeting: tba<br />
<br />
Final meeting and presentation:tba in the ICP meeting room (Allmandring 3, 1st floor, room 1.095).<br />
<br />
Tutorials: tba in the ICP CIP-Pool.<br />
<br />
Deadline for reports: tba<br />
<br />
=== Description ===<br />
<br />
This module focuses on charged matter with electrostatic and hydrodynamic interactions. It should be taken in groups of three people.<br />
It consists of one lecture on electrostatic algorithms, simulations, theory, a presentation and a short report on the simulation results. You only have to give one common presentation<br />
and hand in one report. The Module 3 consists of three parts.<br />
<br />
=== Contact ===<br />
If you have any questions regarding the organisation or content of this module please do not hesitate to contact [[Christian Holm]].<br />
For questions regarding the practical part of the module and technical help contact [[Alexander Reinauer]].<br />
<br />
=== Part 1: Electrostatics ===<br />
==== Description ====<br />
This part is about the theory of electrostatic algorithms for molecular dynamics simulations.<br />
It is concerned with state of the art algorithms beyond the Ewald sum, especially mesh Ewald<br />
methods. To this end the students should read the referenced literature. [[Christian Holm]] will give an hour long lecture. Afterwards we will discuss the content and try to resolve open questions. The presentation should foster the students understanding of the P3M method as well<br />
as give them an overview of its performance compared to other modern electrostatics methods.<br />
<br />
==== Literature ====<br />
:* C. Holm.<br />'''"Simulating Long range interactions".'''<br />''Institute for Computational Physics, Universitat Stuttgart,'' '''2018'''. <br /> [[Media:longrange.pdf|[PDF]]] (15.4 MB) <br /><br />
<bibentry>deserno98a,arnold13b,arnold05a</bibentry><br />
<br />
=== Part 2: Electro-Osmotic Flow ===<br />
==== Description ====<br />
[[File:Slitpore.png|550px|right|Electroosmotic flow in a slit pore]]<br />
This part is practical. It is concerned with the movement of ions in an charged slit pore.<br />
It is similar to the systems that are discussed in the Bachelors thesis of [[Georg Rempfer]]<br />
which is recommended reading. A slit pore consists of two infinite charge walls as shown<br />
in the figure to the right. In this exercise you should simulate such a system with [http://espressomd.org ESPResSo].<br />
You are supposed to use a Lattice Boltzmann fluid coupled to explicit ions which are represented<br />
by charge Week-Chandler-Anderson spheres.<br />
In addition to the charge on the walls, the ions are also subject to an external electrical field parallel to the walls.<br />
Electrostatics should be handled by the P3M algorithm with ELC.<br />
A set of realistic parameters and an more in detail description of the system can be found in the<br />
thesis.<br />
You should measure the flow profile of the fluid and the density and velocity profiles of the ions. The case of the slit<br />
pore can be solved analytically either in the case of only counter ions (the so called salt free case) or in the high<br />
salt limit (Debye-Hueckel-Limit).<br />
Calculate the ion profiles in one or both of these cases and compare the results with the simulation.<br />
<br />
===== Worksheet =====<br />
<br />
Worksheet 2021 {{Download|SS21_adv_sm_mod3_part2.pdf|Detailed worksheet}}<br />
<br />
==== Literature ====<br />
<br />
Some ESPResSo tutorials can be helpful.<br />
* Introductory tutorials, Intermediate tutorials: Lattice-Boltzmann and Charged systems [https://espressomd.github.io/tutorials4.1.4.html Tutorials for ESPResSo 4.1.4] <br />
* The [https://espressomd.github.io/tutorials4.1.4/02-charged_system/02-charged_system-2.html Part 2 of the charged systems tutorial] to see how to setup proper electrostatics in quasi-2D geometry.<br />
<br />
* Georg Rempfer, {{Download|BSc_thesis_rempfer.pdf|"Lattice-Boltzmann Simulations in Complex Geometries"}}, 2010, Institute for Computational Physics, Stuttgart<br />
<br />
=== Part 3: Electrophoresis of Polyelectrolytes ===<br />
==== Description ====<br />
In this part you simulate the movement of a charged polymer under the influence of an external electrical field and hydrodynamic interactions.<br />
Set up a system consisting of a charged polymer, ions with the opposite charge to make the system neutral and an Lattice Boltzmann fluid coupled with <br />
the the ions and polymer. Apply an external field and measure the center of mass velocity of the polymer as a function of the length of the polymer<br />
for polymers of one to 20 monomers. Make sure the system is in equilibrium before you start the sampling. Compare your result to theory and<br />
experimental results (see literature).<br />
<br />
<br />
==== Worksheet ====<br />
{{Download|SS21_adv_sm_mod3_part3.pdf|Detailed worksheet}}<br />
<br />
==== Instructions and Literature ====<br />
General part and part 5 of [[Media:04-lattice_boltzmann.pdf]]<br />
<br />
<bibentry>grass08a, grass09c</bibentry><br />
<br />
=== Report ===<br />
<br />
At the final meeting day of this module, one group will give a presentation about the learned and performed work. In addition, they write a report of about 5 pages containing and discussing the obtained results and hand it in together with the reports of the other modules at the end of the course (see above).<br />
<br />
The final report is due electronically TBA<br />
<br />
<br />
<br />
<!--Please write together one report of 5 to 10 pages containing and discussing your simulation results from part 2 and 3.--></div>Stoveyhttps://www2.icp.uni-stuttgart.de/~icp/mediawiki/index.php?title=Advanced_Simulation_Methods_SS_2022&diff=26103Advanced Simulation Methods SS 20222022-04-10T20:38:01Z<p>Stovey: /* Description */</p>
<hr />
<div>== Overview ==<br />
<br />
;Type<br />
:Lecture and Tutorials (2 SWS in total)<br />
;Lecturer<br />
:Prof. Dr. [[Christian Holm]], PD. Dr. [[Jens Smiatek]], aplProf. Dr. [[Maria Fyta]]<br />
;Course language<br />
:English or German<br />
;Location<br />
:ICP, Allmandring 3; Room: ICP Meeting Room<br />
;Time<br />
:(see below)<br />
The course will consist of three modules supervised by Prof. Dr. [[Christian Holm]], PD. Dr. [[Jens Smiatek]], and aplProf. Dr. [[Maria Fyta]]. It will contain exercises, presentations, discussion meetings, and written reports, worked out in groups. Each group will have to give a talk for all modules.<br />
The students can work in groups. All groups should write a report of about 10 pages on each module, which they should submit to the responsible person for each module by the deadline set for each module.<br />
<br />
<br />
<br />
== Module 1: [[Maria Fyta]] and [[Samuel Tovey]]: Machine-learned Interatomic Potentials ==<br />
<br />
=== Dates ===<br />
<br />
First meeting: Friday, April 23, 2021 at 10:00 (online or in person TBA) <!-- Friday, April 12 at 11:30 --> in the ICP meeting room (Allmandring 3, 1st floor, room 1.095).<br />
<br />
Final meeting and presentation: Friday, May 22; time tba <!--Friday, May 10 at 11:30 --> in the ICP meeting room (Allmandring 3, 1st floor, room 1.095).<br />
<br />
Tutorials: Fridays 11:30-13:00 in the ICP CIP-Pool. The first tutorial will take place on tba <!-- Wed. April 17 at 15:00-16:30.--><br />
<br />
Deadline for reports: tba--><br />
<br />
=== Description ===<br />
<br />
This module focuses on machine-learned interatomic potentials (MLIPs), which reach the accuracy of quantum mechanical computations at a substantially reduced computational cost. MLIPs replace ab initio simulations by mapping a crystal structure or a molecule to properties such as formation enthalpy, elastic constants, or band gaps, etc. Its utility lies in the fact that once the model is trained, properties of new materials can be predicted very quickly. <br />
<br />
<br />
=== Contact ===<br />
<br />
If you have any questions regarding the organization or content of this module please do not hesitate to contact [[Christian Holm]]. For practical guidance regarding the simulations [[Samuel Tovey]].<br />
<br />
=== CP2K ===<br />
<br />
==== Description ====<br />
<br />
=== GAPs ===<br />
<br />
==== Description ====<br />
<br />
This exercise introduces student to the process of developing an inter-atomic potential for liquid argon using machine learning methods. You will follow the process from start to finish, using ab-initio MD methods to construct training data before fitting a model and deploying it in scaled up simulations.<br />
<br />
==== Tutorial ====<br />
<br />
==== Literature ====<br />
<br />
* Bartók, A. P., Payne, M. C., Kondor, R. & Gábor, C. Gaussian approximation potentials: the accuracy of quantum mechanics, without the electrons. Phys. Rev. Lett. 104, 136403 (2010).<br />
<br />
==== Further reading (if interested) ====<br />
<br />
* ...<br />
<br />
== Module 2: [[Jens Smiatek]]: Molecular Theories of Solutions ==<br />
<br />
<br />
=== Dates ===<br />
<br />
First meeting: tba<br />
<br />
Final meeting and presentation: tba.<br />
<br />
Tutorials: tba.<br />
<br />
Deadline for reports: tba<br />
<br />
=== Description ===<br />
<br />
This module focuses on molecular theories of solution. I will outline the main principles of solvation processes and the corresponding interactions. As we will see, most mechanisms strongly differ from highly idealized assumptions such that more effective descriptions are needed. Such considerations are represented by the Kirkwood-Buff theory of solutions or in the framework of conceptual density functional theory. The corresponding theories will be applied for the study of water-ionic liquid mixtures.<br />
<br />
=== Lecture Notes ===<br />
[[Media:Molecular.pdf|Lecture Notes for Part: Molecular Theories of Solution]]<br />
<br />
[[Media:conceptual_DFT.pdf|Lecture Notes for Part: Conceptual Density Functional Theory]]<br />
<br />
=== Contact ===<br />
<br />
If you have any questions regarding the organization or content of this module, please do not hesitate to contact ???.<br />
<br />
=== Part 1: Molecular Theories of Solution ===<br />
<br />
==== Description ====<br />
<br />
This part introduces the students to the field of solution research. An important theory to study solvation and binding behavior is given by the Kirkwood-Buff theory which can be well applied to computer simulations.<br />
The students should study the literature given below and present their findings. The presentation should at a minimum contain an introduction to Kirkwood-Buff theory in the context of the simulations.<br />
<br />
==== Literature ====<br />
<br />
* J. G. Kirkwood and F. P. Buff. "The statistical mechanical theory of solutions. I." J. Chem. Phys. 19, 774 (1951) <br />
* V. Pierce, M. Kang, M. Aburi, S. Weerasinghe and P. E. Smith, "Recent applications of Kirkwood–Buff theory to biological systems", Cell Biochem. Biophys. 50, 1 (2008)<br />
* J. Rösgen, B. M. Pettitt and D. W. Bolen, "Protein folding, stability, and solvation structure in osmolyte solutions", Biophys. J. 89, 2988 (2005)<br />
* J. Smiatek, "Aqueous ionic liquids and their effects on protein structures: an overview on recent theoretical and experimental results", J. Phys. Condens. Matter 29, 233001 (2017)<br />
* E. A. Oprzeska-Zingrebe and J. Smiatek, "Aqueous ionic liquids in comparison with standard co-solutes - Differences and common principles in their interaction with protein and DNA structures", Biophys. Rev. 10, 809 (2018)<br />
* J. Smiatek, A. Heuer and M. Winter, "Properties of ion complexes and their impact on charge transport in organic solvent-based electrolyte solutions for lithium batteries: insights from a theoretical perspective", Batteries 4, 62 (2018)<br />
* T. Kobayashi <i>et al</i>, "The properties of residual water molecules in ionic liquids: a comparison between direct and inverse Kirkwood–Buff approaches", Phys. Chem. Chem. Phys. 19, 18924 (2017)<br />
<br />
=== Part 2: Simulations ===<br />
<br />
==== Description ====<br />
<br />
This part is practical. The simulations will be conducted by the software package GROMACS [http://www.gromacs.org/]. The students will perform the simulations of ionic liquids(IL)-water mixtures at different water concentration in combination with the SPC/E water model and OPLSAA force field for EMImBF4. To generate the initial configuration of the simulation boxes, the software package Packmol [http://www.ime.unicamp.br/~martinez/packmol] will be used.<br />
<br />
First the students simulate pure water and pure IL, and analyze the output data. Following properties will be calculated. The Kirkwood-Buff theory will be used to calculate the Kirkwood-Buff integrals. The student perform the different simulation box size to estimate the proper box size for calculating the properties.<br />
* Kirkwood-Buff integrals<br />
* diffusion coefficients<br />
* mass densities<br />
In addition to above, for water<br />
* hydrogen bond life times and number of hydrogen bonds for water-water pairs<br />
* water mean relaxation times<br />
<br />
Next the student perform the IL-water mixtures at different water concentrations. After energy minimization and warm up, run 500 ns simulations with GROMACS for water mole fractions between X_H2O = 0 - 0.30.<br />
<br />
In comparison to pure water/pure IL, the students will analyze several properties stated above and elucidate their water concentration dependent behavior.<br />
Interpret the corresponding results with regard to the findings in Phys.Chem.Chem.Phys. 19, 18924 (2017). <br />
<br />
All the data needed for the exercise can be found in /group/sm/2019/Advsm_part2 <br />
<br />
<!--<br />
==== Force Fields for IL(EMImBF4) ====<br />
<br />
* {{Download| hectoinzwittmp2.itp |itp-File for Hydroxyectoine}}<br />
* {{Download| hectoinzwittmp2.gro |gro-File for Hydroxyectoine}}<br />
* {{Download| ectoinzwittmp2.itp |itp-File for Ectoine}}<br />
* {{Download| ectoinzwittmp2.gro |gro-File for Ectoine}}<br />
--><br />
<br />
<!--1. Implement the developed force fields for the osmolytes (urea, ectoine and hydroxyectoine) in combination with the SPC/E water model. After energy minimization and warm up, run 20-30 ns simulations with GROMACS for osmolyte concentrations between c = 0 - 6 M.<br />
<br />
2. Study the following properties for the different osmolytes and concentrations:<br />
* diffusion coefficients<br />
* hydrogen bond life times and number of hydrogen bonds for water-water, water-osmolyte and osmolyte-osmolyte pairs<br />
* water mean relaxation times<br />
Interpret the corresponding results. Are the molecules kosmotropes or chaotropes?<br />
<br />
3. Calculate the radial distribution functions for all systems in terms of water-water, water-osmolyte and osmolyte-osmolyte pairs.<br />
Use this information to compute the<br />
<br />
* derivatives of the chemical activity<br />
* derivatives of the activity coefficient<br />
Interpret the corresponding results with regard to the findings in Biochemistry 43, 14472 (2004). <br />
<br />
==== Literature ====<br />
<br />
* D. van der Spoel, P. J. van Maaren, P. Larsson and N. Timneanu, "Thermodynamics of hydrogen bonding in hydrophilic and hydrophobic media", J. Phys. Chem. B 110, 4393 (2006)<br />
* J. Wang, R. M. Wolf, J. W. Caldwell, P. A. Kollman and D. A. Case, "Development and testing of a general Amber force field", J. Comp. Chem. 25, 1157 (2004)<br />
--><br />
<br />
== Module 3: [[Christian Holm]], [[Alexander Reinauer]]: Electrostatics, Lattice Boltzmann, and Electrokinetics==<br />
<br />
<br />
=== Dates ===<br />
<br />
First meeting: tba<br />
<br />
Final meeting and presentation:tba in the ICP meeting room (Allmandring 3, 1st floor, room 1.095).<br />
<br />
Tutorials: tba in the ICP CIP-Pool.<br />
<br />
Deadline for reports: tba<br />
<br />
=== Description ===<br />
<br />
This module focuses on charged matter with electrostatic and hydrodynamic interactions. It should be taken in groups of three people.<br />
It consists of one lecture on electrostatic algorithms, simulations, theory, a presentation and a short report on the simulation results. You only have to give one common presentation<br />
and hand in one report. The Module 3 consists of three parts.<br />
<br />
=== Contact ===<br />
If you have any questions regarding the organisation or content of this module please do not hesitate to contact [[Christian Holm]].<br />
For questions regarding the practical part of the module and technical help contact [[Alexander Reinauer]].<br />
<br />
=== Part 1: Electrostatics ===<br />
==== Description ====<br />
This part is about the theory of electrostatic algorithms for molecular dynamics simulations.<br />
It is concerned with state of the art algorithms beyond the Ewald sum, especially mesh Ewald<br />
methods. To this end the students should read the referenced literature. [[Christian Holm]] will give an hour long lecture. Afterwards we will discuss the content and try to resolve open questions. The presentation should foster the students understanding of the P3M method as well<br />
as give them an overview of its performance compared to other modern electrostatics methods.<br />
<br />
==== Literature ====<br />
:* C. Holm.<br />'''"Simulating Long range interactions".'''<br />''Institute for Computational Physics, Universitat Stuttgart,'' '''2018'''. <br /> [[Media:longrange.pdf|[PDF]]] (15.4 MB) <br /><br />
<bibentry>deserno98a,arnold13b,arnold05a</bibentry><br />
<br />
=== Part 2: Electro-Osmotic Flow ===<br />
==== Description ====<br />
[[File:Slitpore.png|550px|right|Electroosmotic flow in a slit pore]]<br />
This part is practical. It is concerned with the movement of ions in an charged slit pore.<br />
It is similar to the systems that are discussed in the Bachelors thesis of [[Georg Rempfer]]<br />
which is recommended reading. A slit pore consists of two infinite charge walls as shown<br />
in the figure to the right. In this exercise you should simulate such a system with [http://espressomd.org ESPResSo].<br />
You are supposed to use a Lattice Boltzmann fluid coupled to explicit ions which are represented<br />
by charge Week-Chandler-Anderson spheres.<br />
In addition to the charge on the walls, the ions are also subject to an external electrical field parallel to the walls.<br />
Electrostatics should be handled by the P3M algorithm with ELC.<br />
A set of realistic parameters and an more in detail description of the system can be found in the<br />
thesis.<br />
You should measure the flow profile of the fluid and the density and velocity profiles of the ions. The case of the slit<br />
pore can be solved analytically either in the case of only counter ions (the so called salt free case) or in the high<br />
salt limit (Debye-Hueckel-Limit).<br />
Calculate the ion profiles in one or both of these cases and compare the results with the simulation.<br />
<br />
===== Worksheet =====<br />
<br />
Worksheet 2021 {{Download|SS21_adv_sm_mod3_part2.pdf|Detailed worksheet}}<br />
<br />
==== Literature ====<br />
<br />
Some ESPResSo tutorials can be helpful.<br />
* Introductory tutorials, Intermediate tutorials: Lattice-Boltzmann and Charged systems [https://espressomd.github.io/tutorials4.1.4.html Tutorials for ESPResSo 4.1.4] <br />
* The [https://espressomd.github.io/tutorials4.1.4/02-charged_system/02-charged_system-2.html Part 2 of the charged systems tutorial] to see how to setup proper electrostatics in quasi-2D geometry.<br />
<br />
* Georg Rempfer, {{Download|BSc_thesis_rempfer.pdf|"Lattice-Boltzmann Simulations in Complex Geometries"}}, 2010, Institute for Computational Physics, Stuttgart<br />
<br />
=== Part 3: Electrophoresis of Polyelectrolytes ===<br />
==== Description ====<br />
In this part you simulate the movement of a charged polymer under the influence of an external electrical field and hydrodynamic interactions.<br />
Set up a system consisting of a charged polymer, ions with the opposite charge to make the system neutral and an Lattice Boltzmann fluid coupled with <br />
the the ions and polymer. Apply an external field and measure the center of mass velocity of the polymer as a function of the length of the polymer<br />
for polymers of one to 20 monomers. Make sure the system is in equilibrium before you start the sampling. Compare your result to theory and<br />
experimental results (see literature).<br />
<br />
<br />
==== Worksheet ====<br />
{{Download|SS21_adv_sm_mod3_part3.pdf|Detailed worksheet}}<br />
<br />
==== Instructions and Literature ====<br />
General part and part 5 of [[Media:04-lattice_boltzmann.pdf]]<br />
<br />
<bibentry>grass08a, grass09c</bibentry><br />
<br />
=== Report ===<br />
<br />
At the final meeting day of this module, one group will give a presentation about the learned and performed work. In addition, they write a report of about 5 pages containing and discussing the obtained results and hand it in together with the reports of the other modules at the end of the course (see above).<br />
<br />
The final report is due electronically TBA<br />
<br />
<br />
<br />
<!--Please write together one report of 5 to 10 pages containing and discussing your simulation results from part 2 and 3.--></div>Stoveyhttps://www2.icp.uni-stuttgart.de/~icp/mediawiki/index.php?title=Advanced_Simulation_Methods_SS_2022&diff=26102Advanced Simulation Methods SS 20222022-04-10T20:37:53Z<p>Stovey: /* Tutorial */</p>
<hr />
<div>== Overview ==<br />
<br />
;Type<br />
:Lecture and Tutorials (2 SWS in total)<br />
;Lecturer<br />
:Prof. Dr. [[Christian Holm]], PD. Dr. [[Jens Smiatek]], aplProf. Dr. [[Maria Fyta]]<br />
;Course language<br />
:English or German<br />
;Location<br />
:ICP, Allmandring 3; Room: ICP Meeting Room<br />
;Time<br />
:(see below)<br />
The course will consist of three modules supervised by Prof. Dr. [[Christian Holm]], PD. Dr. [[Jens Smiatek]], and aplProf. Dr. [[Maria Fyta]]. It will contain exercises, presentations, discussion meetings, and written reports, worked out in groups. Each group will have to give a talk for all modules.<br />
The students can work in groups. All groups should write a report of about 10 pages on each module, which they should submit to the responsible person for each module by the deadline set for each module.<br />
<br />
<br />
<br />
== Module 1: [[Maria Fyta]] and [[Samuel Tovey]]: Machine-learned Interatomic Potentials ==<br />
<br />
=== Dates ===<br />
<br />
First meeting: Friday, April 23, 2021 at 10:00 (online or in person TBA) <!-- Friday, April 12 at 11:30 --> in the ICP meeting room (Allmandring 3, 1st floor, room 1.095).<br />
<br />
Final meeting and presentation: Friday, May 22; time tba <!--Friday, May 10 at 11:30 --> in the ICP meeting room (Allmandring 3, 1st floor, room 1.095).<br />
<br />
Tutorials: Fridays 11:30-13:00 in the ICP CIP-Pool. The first tutorial will take place on tba <!-- Wed. April 17 at 15:00-16:30.--><br />
<br />
Deadline for reports: tba--><br />
<br />
=== Description ===<br />
<br />
This module focuses on machine-learned interatomic potentials (MLIPs), which reach the accuracy of quantum mechanical computations at a substantially reduced computational cost. MLIPs replace ab initio simulations by mapping a crystal structure or a molecule to properties such as formation enthalpy, elastic constants, or band gaps, etc. Its utility lies in the fact that once the model is trained, properties of new materials can be predicted very quickly. <br />
<br />
<br />
=== Contact ===<br />
<br />
If you have any questions regarding the organization or content of this module please do not hesitate to contact [[Christian Holm]]. For practical guidance regarding the simulations [[Samuel Tovey]].<br />
<br />
=== CP2K ===<br />
<br />
==== Description ====<br />
<br />
=== GAPs ===<br />
<br />
==== Description ====<br />
<br />
This part introduces the students to ...<br />
<br />
==== Tutorial ====<br />
<br />
==== Literature ====<br />
<br />
* Bartók, A. P., Payne, M. C., Kondor, R. & Gábor, C. Gaussian approximation potentials: the accuracy of quantum mechanics, without the electrons. Phys. Rev. Lett. 104, 136403 (2010).<br />
<br />
==== Further reading (if interested) ====<br />
<br />
* ...<br />
<br />
== Module 2: [[Jens Smiatek]]: Molecular Theories of Solutions ==<br />
<br />
<br />
=== Dates ===<br />
<br />
First meeting: tba<br />
<br />
Final meeting and presentation: tba.<br />
<br />
Tutorials: tba.<br />
<br />
Deadline for reports: tba<br />
<br />
=== Description ===<br />
<br />
This module focuses on molecular theories of solution. I will outline the main principles of solvation processes and the corresponding interactions. As we will see, most mechanisms strongly differ from highly idealized assumptions such that more effective descriptions are needed. Such considerations are represented by the Kirkwood-Buff theory of solutions or in the framework of conceptual density functional theory. The corresponding theories will be applied for the study of water-ionic liquid mixtures.<br />
<br />
=== Lecture Notes ===<br />
[[Media:Molecular.pdf|Lecture Notes for Part: Molecular Theories of Solution]]<br />
<br />
[[Media:conceptual_DFT.pdf|Lecture Notes for Part: Conceptual Density Functional Theory]]<br />
<br />
=== Contact ===<br />
<br />
If you have any questions regarding the organization or content of this module, please do not hesitate to contact ???.<br />
<br />
=== Part 1: Molecular Theories of Solution ===<br />
<br />
==== Description ====<br />
<br />
This part introduces the students to the field of solution research. An important theory to study solvation and binding behavior is given by the Kirkwood-Buff theory which can be well applied to computer simulations.<br />
The students should study the literature given below and present their findings. The presentation should at a minimum contain an introduction to Kirkwood-Buff theory in the context of the simulations.<br />
<br />
==== Literature ====<br />
<br />
* J. G. Kirkwood and F. P. Buff. "The statistical mechanical theory of solutions. I." J. Chem. Phys. 19, 774 (1951) <br />
* V. Pierce, M. Kang, M. Aburi, S. Weerasinghe and P. E. Smith, "Recent applications of Kirkwood–Buff theory to biological systems", Cell Biochem. Biophys. 50, 1 (2008)<br />
* J. Rösgen, B. M. Pettitt and D. W. Bolen, "Protein folding, stability, and solvation structure in osmolyte solutions", Biophys. J. 89, 2988 (2005)<br />
* J. Smiatek, "Aqueous ionic liquids and their effects on protein structures: an overview on recent theoretical and experimental results", J. Phys. Condens. Matter 29, 233001 (2017)<br />
* E. A. Oprzeska-Zingrebe and J. Smiatek, "Aqueous ionic liquids in comparison with standard co-solutes - Differences and common principles in their interaction with protein and DNA structures", Biophys. Rev. 10, 809 (2018)<br />
* J. Smiatek, A. Heuer and M. Winter, "Properties of ion complexes and their impact on charge transport in organic solvent-based electrolyte solutions for lithium batteries: insights from a theoretical perspective", Batteries 4, 62 (2018)<br />
* T. Kobayashi <i>et al</i>, "The properties of residual water molecules in ionic liquids: a comparison between direct and inverse Kirkwood–Buff approaches", Phys. Chem. Chem. Phys. 19, 18924 (2017)<br />
<br />
=== Part 2: Simulations ===<br />
<br />
==== Description ====<br />
<br />
This part is practical. The simulations will be conducted by the software package GROMACS [http://www.gromacs.org/]. The students will perform the simulations of ionic liquids(IL)-water mixtures at different water concentration in combination with the SPC/E water model and OPLSAA force field for EMImBF4. To generate the initial configuration of the simulation boxes, the software package Packmol [http://www.ime.unicamp.br/~martinez/packmol] will be used.<br />
<br />
First the students simulate pure water and pure IL, and analyze the output data. Following properties will be calculated. The Kirkwood-Buff theory will be used to calculate the Kirkwood-Buff integrals. The student perform the different simulation box size to estimate the proper box size for calculating the properties.<br />
* Kirkwood-Buff integrals<br />
* diffusion coefficients<br />
* mass densities<br />
In addition to above, for water<br />
* hydrogen bond life times and number of hydrogen bonds for water-water pairs<br />
* water mean relaxation times<br />
<br />
Next the student perform the IL-water mixtures at different water concentrations. After energy minimization and warm up, run 500 ns simulations with GROMACS for water mole fractions between X_H2O = 0 - 0.30.<br />
<br />
In comparison to pure water/pure IL, the students will analyze several properties stated above and elucidate their water concentration dependent behavior.<br />
Interpret the corresponding results with regard to the findings in Phys.Chem.Chem.Phys. 19, 18924 (2017). <br />
<br />
All the data needed for the exercise can be found in /group/sm/2019/Advsm_part2 <br />
<br />
<!--<br />
==== Force Fields for IL(EMImBF4) ====<br />
<br />
* {{Download| hectoinzwittmp2.itp |itp-File for Hydroxyectoine}}<br />
* {{Download| hectoinzwittmp2.gro |gro-File for Hydroxyectoine}}<br />
* {{Download| ectoinzwittmp2.itp |itp-File for Ectoine}}<br />
* {{Download| ectoinzwittmp2.gro |gro-File for Ectoine}}<br />
--><br />
<br />
<!--1. Implement the developed force fields for the osmolytes (urea, ectoine and hydroxyectoine) in combination with the SPC/E water model. After energy minimization and warm up, run 20-30 ns simulations with GROMACS for osmolyte concentrations between c = 0 - 6 M.<br />
<br />
2. Study the following properties for the different osmolytes and concentrations:<br />
* diffusion coefficients<br />
* hydrogen bond life times and number of hydrogen bonds for water-water, water-osmolyte and osmolyte-osmolyte pairs<br />
* water mean relaxation times<br />
Interpret the corresponding results. Are the molecules kosmotropes or chaotropes?<br />
<br />
3. Calculate the radial distribution functions for all systems in terms of water-water, water-osmolyte and osmolyte-osmolyte pairs.<br />
Use this information to compute the<br />
<br />
* derivatives of the chemical activity<br />
* derivatives of the activity coefficient<br />
Interpret the corresponding results with regard to the findings in Biochemistry 43, 14472 (2004). <br />
<br />
==== Literature ====<br />
<br />
* D. van der Spoel, P. J. van Maaren, P. Larsson and N. Timneanu, "Thermodynamics of hydrogen bonding in hydrophilic and hydrophobic media", J. Phys. Chem. B 110, 4393 (2006)<br />
* J. Wang, R. M. Wolf, J. W. Caldwell, P. A. Kollman and D. A. Case, "Development and testing of a general Amber force field", J. Comp. Chem. 25, 1157 (2004)<br />
--><br />
<br />
== Module 3: [[Christian Holm]], [[Alexander Reinauer]]: Electrostatics, Lattice Boltzmann, and Electrokinetics==<br />
<br />
<br />
=== Dates ===<br />
<br />
First meeting: tba<br />
<br />
Final meeting and presentation:tba in the ICP meeting room (Allmandring 3, 1st floor, room 1.095).<br />
<br />
Tutorials: tba in the ICP CIP-Pool.<br />
<br />
Deadline for reports: tba<br />
<br />
=== Description ===<br />
<br />
This module focuses on charged matter with electrostatic and hydrodynamic interactions. It should be taken in groups of three people.<br />
It consists of one lecture on electrostatic algorithms, simulations, theory, a presentation and a short report on the simulation results. You only have to give one common presentation<br />
and hand in one report. The Module 3 consists of three parts.<br />
<br />
=== Contact ===<br />
If you have any questions regarding the organisation or content of this module please do not hesitate to contact [[Christian Holm]].<br />
For questions regarding the practical part of the module and technical help contact [[Alexander Reinauer]].<br />
<br />
=== Part 1: Electrostatics ===<br />
==== Description ====<br />
This part is about the theory of electrostatic algorithms for molecular dynamics simulations.<br />
It is concerned with state of the art algorithms beyond the Ewald sum, especially mesh Ewald<br />
methods. To this end the students should read the referenced literature. [[Christian Holm]] will give an hour long lecture. Afterwards we will discuss the content and try to resolve open questions. The presentation should foster the students understanding of the P3M method as well<br />
as give them an overview of its performance compared to other modern electrostatics methods.<br />
<br />
==== Literature ====<br />
:* C. Holm.<br />'''"Simulating Long range interactions".'''<br />''Institute for Computational Physics, Universitat Stuttgart,'' '''2018'''. <br /> [[Media:longrange.pdf|[PDF]]] (15.4 MB) <br /><br />
<bibentry>deserno98a,arnold13b,arnold05a</bibentry><br />
<br />
=== Part 2: Electro-Osmotic Flow ===<br />
==== Description ====<br />
[[File:Slitpore.png|550px|right|Electroosmotic flow in a slit pore]]<br />
This part is practical. It is concerned with the movement of ions in an charged slit pore.<br />
It is similar to the systems that are discussed in the Bachelors thesis of [[Georg Rempfer]]<br />
which is recommended reading. A slit pore consists of two infinite charge walls as shown<br />
in the figure to the right. In this exercise you should simulate such a system with [http://espressomd.org ESPResSo].<br />
You are supposed to use a Lattice Boltzmann fluid coupled to explicit ions which are represented<br />
by charge Week-Chandler-Anderson spheres.<br />
In addition to the charge on the walls, the ions are also subject to an external electrical field parallel to the walls.<br />
Electrostatics should be handled by the P3M algorithm with ELC.<br />
A set of realistic parameters and an more in detail description of the system can be found in the<br />
thesis.<br />
You should measure the flow profile of the fluid and the density and velocity profiles of the ions. The case of the slit<br />
pore can be solved analytically either in the case of only counter ions (the so called salt free case) or in the high<br />
salt limit (Debye-Hueckel-Limit).<br />
Calculate the ion profiles in one or both of these cases and compare the results with the simulation.<br />
<br />
===== Worksheet =====<br />
<br />
Worksheet 2021 {{Download|SS21_adv_sm_mod3_part2.pdf|Detailed worksheet}}<br />
<br />
==== Literature ====<br />
<br />
Some ESPResSo tutorials can be helpful.<br />
* Introductory tutorials, Intermediate tutorials: Lattice-Boltzmann and Charged systems [https://espressomd.github.io/tutorials4.1.4.html Tutorials for ESPResSo 4.1.4] <br />
* The [https://espressomd.github.io/tutorials4.1.4/02-charged_system/02-charged_system-2.html Part 2 of the charged systems tutorial] to see how to setup proper electrostatics in quasi-2D geometry.<br />
<br />
* Georg Rempfer, {{Download|BSc_thesis_rempfer.pdf|"Lattice-Boltzmann Simulations in Complex Geometries"}}, 2010, Institute for Computational Physics, Stuttgart<br />
<br />
=== Part 3: Electrophoresis of Polyelectrolytes ===<br />
==== Description ====<br />
In this part you simulate the movement of a charged polymer under the influence of an external electrical field and hydrodynamic interactions.<br />
Set up a system consisting of a charged polymer, ions with the opposite charge to make the system neutral and an Lattice Boltzmann fluid coupled with <br />
the the ions and polymer. Apply an external field and measure the center of mass velocity of the polymer as a function of the length of the polymer<br />
for polymers of one to 20 monomers. Make sure the system is in equilibrium before you start the sampling. Compare your result to theory and<br />
experimental results (see literature).<br />
<br />
<br />
==== Worksheet ====<br />
{{Download|SS21_adv_sm_mod3_part3.pdf|Detailed worksheet}}<br />
<br />
==== Instructions and Literature ====<br />
General part and part 5 of [[Media:04-lattice_boltzmann.pdf]]<br />
<br />
<bibentry>grass08a, grass09c</bibentry><br />
<br />
=== Report ===<br />
<br />
At the final meeting day of this module, one group will give a presentation about the learned and performed work. In addition, they write a report of about 5 pages containing and discussing the obtained results and hand it in together with the reports of the other modules at the end of the course (see above).<br />
<br />
The final report is due electronically TBA<br />
<br />
<br />
<br />
<!--Please write together one report of 5 to 10 pages containing and discussing your simulation results from part 2 and 3.--></div>Stoveyhttps://www2.icp.uni-stuttgart.de/~icp/mediawiki/index.php?title=Advanced_Simulation_Methods_SS_2022&diff=26101Advanced Simulation Methods SS 20222022-04-10T20:37:32Z<p>Stovey: /* Tutorial */</p>
<hr />
<div>== Overview ==<br />
<br />
;Type<br />
:Lecture and Tutorials (2 SWS in total)<br />
;Lecturer<br />
:Prof. Dr. [[Christian Holm]], PD. Dr. [[Jens Smiatek]], aplProf. Dr. [[Maria Fyta]]<br />
;Course language<br />
:English or German<br />
;Location<br />
:ICP, Allmandring 3; Room: ICP Meeting Room<br />
;Time<br />
:(see below)<br />
The course will consist of three modules supervised by Prof. Dr. [[Christian Holm]], PD. Dr. [[Jens Smiatek]], and aplProf. Dr. [[Maria Fyta]]. It will contain exercises, presentations, discussion meetings, and written reports, worked out in groups. Each group will have to give a talk for all modules.<br />
The students can work in groups. All groups should write a report of about 10 pages on each module, which they should submit to the responsible person for each module by the deadline set for each module.<br />
<br />
<br />
<br />
== Module 1: [[Maria Fyta]] and [[Samuel Tovey]]: Machine-learned Interatomic Potentials ==<br />
<br />
=== Dates ===<br />
<br />
First meeting: Friday, April 23, 2021 at 10:00 (online or in person TBA) <!-- Friday, April 12 at 11:30 --> in the ICP meeting room (Allmandring 3, 1st floor, room 1.095).<br />
<br />
Final meeting and presentation: Friday, May 22; time tba <!--Friday, May 10 at 11:30 --> in the ICP meeting room (Allmandring 3, 1st floor, room 1.095).<br />
<br />
Tutorials: Fridays 11:30-13:00 in the ICP CIP-Pool. The first tutorial will take place on tba <!-- Wed. April 17 at 15:00-16:30.--><br />
<br />
Deadline for reports: tba--><br />
<br />
=== Description ===<br />
<br />
This module focuses on machine-learned interatomic potentials (MLIPs), which reach the accuracy of quantum mechanical computations at a substantially reduced computational cost. MLIPs replace ab initio simulations by mapping a crystal structure or a molecule to properties such as formation enthalpy, elastic constants, or band gaps, etc. Its utility lies in the fact that once the model is trained, properties of new materials can be predicted very quickly. <br />
<br />
<br />
=== Contact ===<br />
<br />
If you have any questions regarding the organization or content of this module please do not hesitate to contact [[Christian Holm]]. For practical guidance regarding the simulations [[Samuel Tovey]].<br />
<br />
=== CP2K ===<br />
<br />
==== Description ====<br />
<br />
=== GAPs ===<br />
<br />
==== Description ====<br />
<br />
This part introduces the students to ...<br />
<br />
==== Tutorial ====<br />
<br />
This exercise introduces student to the process of developing an inter-atomic potential for liquid argon using machine learning methods. You will follow the process from start to finish, using ab-initio MD methods to construct training data before fitting a model and deploying it in scaled up simulations.<br />
<br />
==== Literature ====<br />
<br />
* Bartók, A. P., Payne, M. C., Kondor, R. & Gábor, C. Gaussian approximation potentials: the accuracy of quantum mechanics, without the electrons. Phys. Rev. Lett. 104, 136403 (2010).<br />
<br />
==== Further reading (if interested) ====<br />
<br />
* ...<br />
<br />
== Module 2: [[Jens Smiatek]]: Molecular Theories of Solutions ==<br />
<br />
<br />
=== Dates ===<br />
<br />
First meeting: tba<br />
<br />
Final meeting and presentation: tba.<br />
<br />
Tutorials: tba.<br />
<br />
Deadline for reports: tba<br />
<br />
=== Description ===<br />
<br />
This module focuses on molecular theories of solution. I will outline the main principles of solvation processes and the corresponding interactions. As we will see, most mechanisms strongly differ from highly idealized assumptions such that more effective descriptions are needed. Such considerations are represented by the Kirkwood-Buff theory of solutions or in the framework of conceptual density functional theory. The corresponding theories will be applied for the study of water-ionic liquid mixtures.<br />
<br />
=== Lecture Notes ===<br />
[[Media:Molecular.pdf|Lecture Notes for Part: Molecular Theories of Solution]]<br />
<br />
[[Media:conceptual_DFT.pdf|Lecture Notes for Part: Conceptual Density Functional Theory]]<br />
<br />
=== Contact ===<br />
<br />
If you have any questions regarding the organization or content of this module, please do not hesitate to contact ???.<br />
<br />
=== Part 1: Molecular Theories of Solution ===<br />
<br />
==== Description ====<br />
<br />
This part introduces the students to the field of solution research. An important theory to study solvation and binding behavior is given by the Kirkwood-Buff theory which can be well applied to computer simulations.<br />
The students should study the literature given below and present their findings. The presentation should at a minimum contain an introduction to Kirkwood-Buff theory in the context of the simulations.<br />
<br />
==== Literature ====<br />
<br />
* J. G. Kirkwood and F. P. Buff. "The statistical mechanical theory of solutions. I." J. Chem. Phys. 19, 774 (1951) <br />
* V. Pierce, M. Kang, M. Aburi, S. Weerasinghe and P. E. Smith, "Recent applications of Kirkwood–Buff theory to biological systems", Cell Biochem. Biophys. 50, 1 (2008)<br />
* J. Rösgen, B. M. Pettitt and D. W. Bolen, "Protein folding, stability, and solvation structure in osmolyte solutions", Biophys. J. 89, 2988 (2005)<br />
* J. Smiatek, "Aqueous ionic liquids and their effects on protein structures: an overview on recent theoretical and experimental results", J. Phys. Condens. Matter 29, 233001 (2017)<br />
* E. A. Oprzeska-Zingrebe and J. Smiatek, "Aqueous ionic liquids in comparison with standard co-solutes - Differences and common principles in their interaction with protein and DNA structures", Biophys. Rev. 10, 809 (2018)<br />
* J. Smiatek, A. Heuer and M. Winter, "Properties of ion complexes and their impact on charge transport in organic solvent-based electrolyte solutions for lithium batteries: insights from a theoretical perspective", Batteries 4, 62 (2018)<br />
* T. Kobayashi <i>et al</i>, "The properties of residual water molecules in ionic liquids: a comparison between direct and inverse Kirkwood–Buff approaches", Phys. Chem. Chem. Phys. 19, 18924 (2017)<br />
<br />
=== Part 2: Simulations ===<br />
<br />
==== Description ====<br />
<br />
This part is practical. The simulations will be conducted by the software package GROMACS [http://www.gromacs.org/]. The students will perform the simulations of ionic liquids(IL)-water mixtures at different water concentration in combination with the SPC/E water model and OPLSAA force field for EMImBF4. To generate the initial configuration of the simulation boxes, the software package Packmol [http://www.ime.unicamp.br/~martinez/packmol] will be used.<br />
<br />
First the students simulate pure water and pure IL, and analyze the output data. Following properties will be calculated. The Kirkwood-Buff theory will be used to calculate the Kirkwood-Buff integrals. The student perform the different simulation box size to estimate the proper box size for calculating the properties.<br />
* Kirkwood-Buff integrals<br />
* diffusion coefficients<br />
* mass densities<br />
In addition to above, for water<br />
* hydrogen bond life times and number of hydrogen bonds for water-water pairs<br />
* water mean relaxation times<br />
<br />
Next the student perform the IL-water mixtures at different water concentrations. After energy minimization and warm up, run 500 ns simulations with GROMACS for water mole fractions between X_H2O = 0 - 0.30.<br />
<br />
In comparison to pure water/pure IL, the students will analyze several properties stated above and elucidate their water concentration dependent behavior.<br />
Interpret the corresponding results with regard to the findings in Phys.Chem.Chem.Phys. 19, 18924 (2017). <br />
<br />
All the data needed for the exercise can be found in /group/sm/2019/Advsm_part2 <br />
<br />
<!--<br />
==== Force Fields for IL(EMImBF4) ====<br />
<br />
* {{Download| hectoinzwittmp2.itp |itp-File for Hydroxyectoine}}<br />
* {{Download| hectoinzwittmp2.gro |gro-File for Hydroxyectoine}}<br />
* {{Download| ectoinzwittmp2.itp |itp-File for Ectoine}}<br />
* {{Download| ectoinzwittmp2.gro |gro-File for Ectoine}}<br />
--><br />
<br />
<!--1. Implement the developed force fields for the osmolytes (urea, ectoine and hydroxyectoine) in combination with the SPC/E water model. After energy minimization and warm up, run 20-30 ns simulations with GROMACS for osmolyte concentrations between c = 0 - 6 M.<br />
<br />
2. Study the following properties for the different osmolytes and concentrations:<br />
* diffusion coefficients<br />
* hydrogen bond life times and number of hydrogen bonds for water-water, water-osmolyte and osmolyte-osmolyte pairs<br />
* water mean relaxation times<br />
Interpret the corresponding results. Are the molecules kosmotropes or chaotropes?<br />
<br />
3. Calculate the radial distribution functions for all systems in terms of water-water, water-osmolyte and osmolyte-osmolyte pairs.<br />
Use this information to compute the<br />
<br />
* derivatives of the chemical activity<br />
* derivatives of the activity coefficient<br />
Interpret the corresponding results with regard to the findings in Biochemistry 43, 14472 (2004). <br />
<br />
==== Literature ====<br />
<br />
* D. van der Spoel, P. J. van Maaren, P. Larsson and N. Timneanu, "Thermodynamics of hydrogen bonding in hydrophilic and hydrophobic media", J. Phys. Chem. B 110, 4393 (2006)<br />
* J. Wang, R. M. Wolf, J. W. Caldwell, P. A. Kollman and D. A. Case, "Development and testing of a general Amber force field", J. Comp. Chem. 25, 1157 (2004)<br />
--><br />
<br />
== Module 3: [[Christian Holm]], [[Alexander Reinauer]]: Electrostatics, Lattice Boltzmann, and Electrokinetics==<br />
<br />
<br />
=== Dates ===<br />
<br />
First meeting: tba<br />
<br />
Final meeting and presentation:tba in the ICP meeting room (Allmandring 3, 1st floor, room 1.095).<br />
<br />
Tutorials: tba in the ICP CIP-Pool.<br />
<br />
Deadline for reports: tba<br />
<br />
=== Description ===<br />
<br />
This module focuses on charged matter with electrostatic and hydrodynamic interactions. It should be taken in groups of three people.<br />
It consists of one lecture on electrostatic algorithms, simulations, theory, a presentation and a short report on the simulation results. You only have to give one common presentation<br />
and hand in one report. The Module 3 consists of three parts.<br />
<br />
=== Contact ===<br />
If you have any questions regarding the organisation or content of this module please do not hesitate to contact [[Christian Holm]].<br />
For questions regarding the practical part of the module and technical help contact [[Alexander Reinauer]].<br />
<br />
=== Part 1: Electrostatics ===<br />
==== Description ====<br />
This part is about the theory of electrostatic algorithms for molecular dynamics simulations.<br />
It is concerned with state of the art algorithms beyond the Ewald sum, especially mesh Ewald<br />
methods. To this end the students should read the referenced literature. [[Christian Holm]] will give an hour long lecture. Afterwards we will discuss the content and try to resolve open questions. The presentation should foster the students understanding of the P3M method as well<br />
as give them an overview of its performance compared to other modern electrostatics methods.<br />
<br />
==== Literature ====<br />
:* C. Holm.<br />'''"Simulating Long range interactions".'''<br />''Institute for Computational Physics, Universitat Stuttgart,'' '''2018'''. <br /> [[Media:longrange.pdf|[PDF]]] (15.4 MB) <br /><br />
<bibentry>deserno98a,arnold13b,arnold05a</bibentry><br />
<br />
=== Part 2: Electro-Osmotic Flow ===<br />
==== Description ====<br />
[[File:Slitpore.png|550px|right|Electroosmotic flow in a slit pore]]<br />
This part is practical. It is concerned with the movement of ions in an charged slit pore.<br />
It is similar to the systems that are discussed in the Bachelors thesis of [[Georg Rempfer]]<br />
which is recommended reading. A slit pore consists of two infinite charge walls as shown<br />
in the figure to the right. In this exercise you should simulate such a system with [http://espressomd.org ESPResSo].<br />
You are supposed to use a Lattice Boltzmann fluid coupled to explicit ions which are represented<br />
by charge Week-Chandler-Anderson spheres.<br />
In addition to the charge on the walls, the ions are also subject to an external electrical field parallel to the walls.<br />
Electrostatics should be handled by the P3M algorithm with ELC.<br />
A set of realistic parameters and an more in detail description of the system can be found in the<br />
thesis.<br />
You should measure the flow profile of the fluid and the density and velocity profiles of the ions. The case of the slit<br />
pore can be solved analytically either in the case of only counter ions (the so called salt free case) or in the high<br />
salt limit (Debye-Hueckel-Limit).<br />
Calculate the ion profiles in one or both of these cases and compare the results with the simulation.<br />
<br />
===== Worksheet =====<br />
<br />
Worksheet 2021 {{Download|SS21_adv_sm_mod3_part2.pdf|Detailed worksheet}}<br />
<br />
==== Literature ====<br />
<br />
Some ESPResSo tutorials can be helpful.<br />
* Introductory tutorials, Intermediate tutorials: Lattice-Boltzmann and Charged systems [https://espressomd.github.io/tutorials4.1.4.html Tutorials for ESPResSo 4.1.4] <br />
* The [https://espressomd.github.io/tutorials4.1.4/02-charged_system/02-charged_system-2.html Part 2 of the charged systems tutorial] to see how to setup proper electrostatics in quasi-2D geometry.<br />
<br />
* Georg Rempfer, {{Download|BSc_thesis_rempfer.pdf|"Lattice-Boltzmann Simulations in Complex Geometries"}}, 2010, Institute for Computational Physics, Stuttgart<br />
<br />
=== Part 3: Electrophoresis of Polyelectrolytes ===<br />
==== Description ====<br />
In this part you simulate the movement of a charged polymer under the influence of an external electrical field and hydrodynamic interactions.<br />
Set up a system consisting of a charged polymer, ions with the opposite charge to make the system neutral and an Lattice Boltzmann fluid coupled with <br />
the the ions and polymer. Apply an external field and measure the center of mass velocity of the polymer as a function of the length of the polymer<br />
for polymers of one to 20 monomers. Make sure the system is in equilibrium before you start the sampling. Compare your result to theory and<br />
experimental results (see literature).<br />
<br />
<br />
==== Worksheet ====<br />
{{Download|SS21_adv_sm_mod3_part3.pdf|Detailed worksheet}}<br />
<br />
==== Instructions and Literature ====<br />
General part and part 5 of [[Media:04-lattice_boltzmann.pdf]]<br />
<br />
<bibentry>grass08a, grass09c</bibentry><br />
<br />
=== Report ===<br />
<br />
At the final meeting day of this module, one group will give a presentation about the learned and performed work. In addition, they write a report of about 5 pages containing and discussing the obtained results and hand it in together with the reports of the other modules at the end of the course (see above).<br />
<br />
The final report is due electronically TBA<br />
<br />
<br />
<br />
<!--Please write together one report of 5 to 10 pages containing and discussing your simulation results from part 2 and 3.--></div>Stoveyhttps://www2.icp.uni-stuttgart.de/~icp/mediawiki/index.php?title=Samuel_Tovey&diff=25874Samuel Tovey2021-07-21T14:22:18Z<p>Stovey: </p>
<hr />
<div>{{Person<br />
|name=Tovey, Samuel<br />
|status=PhD student<br />
|phone=<br />
|room=1.076<br />
|email=stovey<br />
|category=holm<br />
|topical=machine_learning<br />
}}<br />
<br />
I am a PhD student in [[Christian Holm]]'s group, working on applications of machine learning in simulation science.<br />
<br />
=== Publications ===<br />
<bibentry>tovey20a</bibentry><br />
<br />
=== Master's Thesis ===<br />
During my masters thesis I developed an interatomic potential for molten NaCl and LiF using a machine learning method<br />
known as Gaussian Process Regression (GPR). The models were developed using data from Density Functional Theory (DFT)<br />
simulations.</div>Stoveyhttps://www2.icp.uni-stuttgart.de/~icp/mediawiki/index.php?title=Fabian_Zills&diff=25873Fabian Zills2021-07-21T14:21:38Z<p>Stovey: Created page with "{{Person |name=Zills, Fabian |status=Masters Student |phone= |room=1.076 |email=fzills |category=holm |topical=machine_learning }} I am a masters student in the group of Prof..."</p>
<hr />
<div>{{Person<br />
|name=Zills, Fabian<br />
|status=Masters Student<br />
|phone=<br />
|room=1.076<br />
|email=fzills<br />
|category=holm<br />
|topical=machine_learning<br />
}}<br />
<br />
I am a masters student in the group of Professor Holm studying the development of machine learned inter-atomic potentials.</div>Stoveyhttps://www2.icp.uni-stuttgart.de/~icp/mediawiki/index.php?title=Marco_Br%C3%BCckner&diff=25814Marco Brückner2021-06-18T20:31:28Z<p>Stovey: </p>
<hr />
<div>{{Person<br />
|name=Bruekner, Marco<br />
|phone=67703<br />
|room=01.039<br />
|status=Master Student<br />
|email=mbrueckner<br />
|category=holm<br />
|topical=machine_learning<br />
|image=mbrueckner.jpg<br />
}}</div>Stoveyhttps://www2.icp.uni-stuttgart.de/~icp/mediawiki/index.php?title=Hauptseminar_Porous_Media_SS_2021/Machine_learning_in_physics&diff=25533Hauptseminar Porous Media SS 2021/Machine learning in physics2021-02-09T13:44:07Z<p>Stovey: /* Important Points */</p>
<hr />
<div>{{Seminartopic<br />
|number=3<br />
|topic=Machine learning for the construction of force fields<br />
|date=TBA<br />
|time=TBA<br />
|speaker=TBA<br />
|tutor=[[Samuel Tovey]]<br />
|handout=<br />
}}<br />
<br />
== Contents ==<br />
<br />
Description: In recent years machine learning methods have been employed to develop non-parametric models for the approximation of the potential energy surface. <br />
These models, trained on ab-initio simulation data, deliver the accuracy of these ab-initio methods at the speed and system size of classical approaches. <br />
Development of these models requires several important steps, all of which are under active study and for which many approaches exist. Such steps include selecting <br />
training data, representing this data, and the choosing machine learning model to use. <br />
This talk will address current approaches for developing these models as well as well as some of the results that have been found by using the machine learning methods. <br />
The presenter will build upon the foundation work of the previous talks on DFT methods and atomistic methods in order to demonstrate the position of the machine learning <br />
approaches as a bridge between them.<br />
<br />
=== Important Points ===<br />
<br />
* Gaussian process regression and Neural network potentials<br />
* Sampling configuration space for training data<br />
* Representation of molecular environments<br />
* Failures in ML potentials<br />
* Study of chemical reactions on an atomistic scale<br />
<br />
== Literature ==<br />
<br />
<bibentry pdflink="yes"><br />
bartok15a<br />
ko21a<br />
langer20a<br />
chmiela18a<br />
chmiela19a<br />
rupp15b<br />
</bibentry></div>Stoveyhttps://www2.icp.uni-stuttgart.de/~icp/mediawiki/index.php?title=Hauptseminar_Porous_Media_SS_2021/Machine_learning_in_physics&diff=25496Hauptseminar Porous Media SS 2021/Machine learning in physics2021-02-05T17:28:29Z<p>Stovey: </p>
<hr />
<div>{{Seminartopic<br />
|number=3<br />
|topic=Machine learning for the construction of force fields<br />
|date=TBA<br />
|time=TBA<br />
|speaker=TBA<br />
|tutor=[[Samuel Tovey]]<br />
|handout=<br />
}}<br />
<br />
== Contents ==<br />
<br />
Description: In recent years machine learning methods have been employed to develop non-parametric models for the approximation of the potential energy surface. <br />
These models, trained on ab-initio simulation data, deliver the accuracy of these ab-initio methods at the speed and system size of classical approaches. <br />
Development of these models requires several important steps, all of which are under active study and for which many approaches exist. Such steps include selecting <br />
training data, representing this data, and the choosing machine learning model to use. <br />
This talk will address current approaches for developing these models as well as well as some of the results that have been found by using the machine learning methods. <br />
The presenter will build upon the foundation work of the previous talks on DFT methods and atomistic methods in order to demonstrate the position of the machine learning <br />
approaches as a bridge between them.<br />
<br />
=== Important Points ===<br />
<br />
* Gaussian process regression and Neural network potentials<br />
* Sampling configuration space for training data<br />
* Representation of molecular environments<br />
* Failures in ML potentials<br />
<br />
== Literature ==<br />
<br />
<bibentry pdflink="yes"><br />
bartok15a<br />
ko21a<br />
langer20a<br />
chmiela18a<br />
chmiela19a<br />
rupp15b<br />
</bibentry></div>Stoveyhttps://www2.icp.uni-stuttgart.de/~icp/mediawiki/index.php?title=Hauptseminar_Porous_Media_SS_2021/Machine_learning_in_physics&diff=25489Hauptseminar Porous Media SS 2021/Machine learning in physics2021-02-05T12:54:34Z<p>Stovey: /* Literature */</p>
<hr />
<div>{{Seminartopic<br />
|number=3<br />
|topic=Machine learned force fields<br />
|date=TBA<br />
|time=TBA<br />
|speaker=TBA<br />
|tutor=[[Samuel Tovey]]<br />
|handout=<br />
}}<br />
<br />
== Contents ==<br />
<br />
Description: In recent years machine learning methods have been employed to develop non-parametric models for the approximation of the potential energy surface. <br />
These models, trained on ab-initio simulation data, deliver the accuracy of these ab-initio methods at the speed and system size of classical approaches. <br />
Development of these models requires several important steps, all of which are under active study and for which many approaches exist. Such steps include selecting <br />
training data, representing this data, and the choosing machine learning model to use. <br />
This talk will address current approaches for developing these models as well as well as some of the results that have been found by using the machine learning methods. <br />
The presenter will build upon the foundation work of the previous talks on DFT methods and atomistic methods in order to demonstrate the position of the machine learning <br />
approaches as a bridge between them.<br />
<br />
=== Important Points ===<br />
<br />
* Gaussian process regression and Neural network potentials<br />
* Sampling configuration space for training data<br />
* Representation of molecular environments<br />
* Failures in ML potentials<br />
<br />
== Literature ==<br />
<br />
<bibentry pdflink="yes"><br />
bartok15a<br />
ko21a<br />
langer20a<br />
chmiela18a<br />
chmiela19a<br />
rupp15b<br />
</bibentry></div>Stoveyhttps://www2.icp.uni-stuttgart.de/~icp/mediawiki/index.php?title=Hauptseminar_Porous_Media_SS_2021/Machine_learning_in_physics&diff=25488Hauptseminar Porous Media SS 2021/Machine learning in physics2021-02-05T12:40:31Z<p>Stovey: /* Contents */</p>
<hr />
<div>{{Seminartopic<br />
|number=3<br />
|topic=Machine learned force fields<br />
|date=TBA<br />
|time=TBA<br />
|speaker=TBA<br />
|tutor=[[Samuel Tovey]]<br />
|handout=<br />
}}<br />
<br />
== Contents ==<br />
<br />
Description: In recent years machine learning methods have been employed to develop non-parametric models for the approximation of the potential energy surface. <br />
These models, trained on ab-initio simulation data, deliver the accuracy of these ab-initio methods at the speed and system size of classical approaches. <br />
Development of these models requires several important steps, all of which are under active study and for which many approaches exist. Such steps include selecting <br />
training data, representing this data, and the choosing machine learning model to use. <br />
This talk will address current approaches for developing these models as well as well as some of the results that have been found by using the machine learning methods. <br />
The presenter will build upon the foundation work of the previous talks on DFT methods and atomistic methods in order to demonstrate the position of the machine learning <br />
approaches as a bridge between them.<br />
<br />
=== Important Points ===<br />
<br />
* Gaussian process regression and Neural network potentials<br />
* Sampling configuration space for training data<br />
* Representation of molecular environments<br />
* Failures in ML potentials<br />
<br />
== Literature ==<br />
<br />
TBA</div>Stoveyhttps://www2.icp.uni-stuttgart.de/~icp/mediawiki/index.php?title=Samuel_Tovey&diff=25199Samuel Tovey2020-11-20T14:56:47Z<p>Stovey: </p>
<hr />
<div>{{Person<br />
|name=Tovey, Samuel<br />
|status=PhD student<br />
|phone=<br />
|room=1.076<br />
|email=stovey<br />
|category=holm<br />
|topical=machine_learning<br />
}}<br />
<br />
I am a PhD student in [[Christian Holm]]'s group, working on applications of machine learning in simulation science.<br />
<br />
=== Publications ===<br />
<bibentry>tovey20a</bibentry><br />
<br />
=== Master's Thesis ===<br />
During my masters thesis I developed and interatomic potential for molten NaCl and LiF using a machine learning method<br />
known as Gaussian Process Regression (GPR). The models were developed using data from Density Functional Theory (DFT)<br />
simulations.</div>Stoveyhttps://www2.icp.uni-stuttgart.de/~icp/mediawiki/index.php?title=Samuel_Tovey&diff=25198Samuel Tovey2020-11-20T14:55:34Z<p>Stovey: </p>
<hr />
<div>{{Person<br />
|name=Tovey, Samuel<br />
|status=PhD student<br />
|phone=<br />
|room=1.076<br />
|email=stovey<br />
|category=holm<br />
|topical=machine_learning<br />
}}<br />
I am a PhD student in [[Christian Holm]]'s group, working on applications of machine learning in simulation science.<br />
<br />
=== Publications ===<br />
<bibentry>tovey20a</bibentry><br />
<br />
=== Master's Thesis ===<br />
During my masters thesis I developed and interatomic potential for molten NaCl and LiF using a machine learning method<br />
known as Gaussian Process Regression (GPR). The models were developed using data from Density Functional Theory (DFT)<br />
simulations.</div>Stoveyhttps://www2.icp.uni-stuttgart.de/~icp/mediawiki/index.php?title=Samuel_Tovey&diff=25197Samuel Tovey2020-11-20T14:53:53Z<p>Stovey: </p>
<hr />
<div>{{Person<br />
|name=Tovey, Samuel<br />
|status=PhD student<br />
|phone=<br />
|room=1.076<br />
|email=stovey<br />
|category=holm<br />
|topical=machine_learning<br />
}}<br />
I am a PhD student in [[Christian Holm]]'s group, working on applications of machine learning in simulation science.<br />
<br />
=== Publications ===<br />
<bibentry>tovey20a</bibentry></div>Stoveyhttps://www2.icp.uni-stuttgart.de/~icp/mediawiki/index.php?title=Samuel_Tovey&diff=25196Samuel Tovey2020-11-20T14:52:19Z<p>Stovey: </p>
<hr />
<div>{{Person<br />
|name=Tovey, Samuel<br />
|status=PhD student<br />
|phone=<br />
|room=1.076<br />
|email=stovey<br />
|category=holm<br />
|topical=machine_learning<br />
}}<br />
I am a PhD student in [[Christian Holm]]'s group, working on applications of machine learning in simulation science.</div>Stoveyhttps://www2.icp.uni-stuttgart.de/~icp/mediawiki/index.php?title=Samuel_Tovey&diff=25195Samuel Tovey2020-11-20T14:50:56Z<p>Stovey: </p>
<hr />
<div>{{Person<br />
|name=Tovey, Samuel<br />
|status=PhD student<br />
|phone=<br />
|room=1.076<br />
|email=stovey<br />
|category=holm<br />
|topical=machine_learning<br />
}}</div>Stoveyhttps://www2.icp.uni-stuttgart.de/~icp/mediawiki/index.php?title=Property:Topical_Meeting&diff=25194Property:Topical Meeting2020-11-20T14:49:17Z<p>Stovey: </p>
<hr />
<div>The [[Internal:Topical meetings|Topical meeting]] of a person, of type [[has type::String]]. It allows for the following values:<br />
* [[Allows value::none]]<br />
* [[Allows value::undefined]]<br />
* [[Allows value::espresso]]<br />
* [[Allows value::electrokinetics]]<br />
* [[Allows value::nanopore]]<br />
* [[Allows value::gel]]<br />
* [[Allows value::atomistics]]<br />
* [[Allows value::sampling]]<br />
* [[Allows value::hydrodynamics]]<br />
* [[Allows value::ionic_liquids]]<br />
* [[Allows value::active]]<br />
* [[Allows value::machine_learning]]</div>Stoveyhttps://www2.icp.uni-stuttgart.de/~icp/mediawiki/index.php?title=Samuel_Tovey&diff=25193Samuel Tovey2020-11-20T14:48:14Z<p>Stovey: </p>
<hr />
<div>{{Person<br />
|name=Tovey, Samuel<br />
|status=PhD student<br />
|phone=<br />
|room=1.076<br />
|email=stovey<br />
|category=holm<br />
|topical=machine learning<br />
}}</div>Stovey