Difference between revisions of "Category:Machine Learning"

This section describes the methodology used for force-field generation using machine learning. One first should checkout the theoretical background Machine learning force field: Theory together with the basic description how to run the machine learning calculations Machine learning force field calculations: Basics. Then gain some hands-on experience with the following tutorial: Liquid Si - MLFF.

Input

• Besides the usual input files (INCAR, POSCAR, etc.) the machine learning force field method requires the following input files:
  • ML_AB Ab initio data used to create the training data.
  • ML_FF File containing force field parameters.

Output

• The machine learning force field method generates the following output files:
  • ML_LOGFILE Main output file for the machine learning force field method.
  • ML_ABN New abinitio data (used as ML_AB in the next run).
  • ML_REG Output file summarizing regression results.
  • ML_HIS Output file summarizing the histogram data.
  • ML_FFN File containing new force field parameters (used as ML_FF in the next run).
  • ML_EATOM Output file containing local atomic energies
  • ML_HEAT Output file including local heat flux

All INCAR tags belonging to the machine learning force field method start with the prefix ML_FF_. Input tags that are related to the many-body term end with _MB.

Theoretical Background

• Machine learning force field: Theory.

How to

• Machine learning force field calculations: Basics.
• Machine learning force field calculations: Intermediate.

Tutorial

• Basic tutorial to learn how to perform on-the-fly learning and how to control the accuracy of the force field: Liquid Si - MLFF.