Category:Machine Learning

This section describes the methodology used for force-field generation using machine learning. One first should checkout the theoretical background On-the-fly machine learning force field generation using Bayesian linear regression together with the basic description how to run the machine learning calculations Machine learning force field calculations: Basics.

Input

• Besides the usual input files (INCAR, POSCAR, etc.) the machine learning force field method requires the following input files:
  • ML_ABCAR Ab initio data used to create the training data.
  • ML_FFCAR 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_ABNCAR New abinitio data (used as ABCAR in the next run).
  • ML_REGCAR Output file summarizing regression results.
  • ML_HISCAR Output file summarizing the histogram data.
  • ML_FFNCAR File containing new force field parameters (used as FFCAR 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

• On-the-fly machine learning force field generation using Bayesian linear regression.

How to

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