# Difference between revisions of "Hauptseminar Porous Media SS 2021/Machine learning in physics"

Line 11: | Line 11: | ||

== Contents == | == Contents == | ||

− | + | Description: In recent years machine learning methods have been employed to develop non-parametric models for the approximation of the potential energy surface. | |

+ | 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. | ||

+ | 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 | ||

+ | training data, representing this data, and the choosing machine learning model to use. | ||

+ | 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. | ||

+ | 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 | ||

+ | approaches as a bridge between them. | ||

+ | |||

+ | === Important Points === | ||

+ | |||

+ | * Gaussian process regression and Neural network potentials | ||

+ | * Sampling configuration space for training data | ||

+ | * Representation of molecular environments | ||

+ | * Failures in ML potentials | ||

== Literature == | == Literature == | ||

TBA | TBA |

## Revision as of 13:40, 5 February 2021

- Date
- TBA"TBA" contains an extrinsic dash or other characters that are invalid for a date interpretation.
- Time
- TBA
- Topic
- Machine learned force fields
- Speaker
- TBA
- Tutor
- Samuel Tovey

## Contents

Description: In recent years machine learning methods have been employed to develop non-parametric models for the approximation of the potential energy surface. 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. 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 training data, representing this data, and the choosing machine learning model to use. 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. 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 approaches as a bridge between them.

### Important Points

- Gaussian process regression and Neural network potentials
- Sampling configuration space for training data
- Representation of molecular environments
- Failures in ML potentials

## Literature

TBA