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Difference between revisions of "Category:Machine Learning"

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[[Category:VASP|Machine Learning]][[Category:VASP6]]
[[Category:VASP|Machine Learning]]

Revision as of 10:30, 30 January 2020

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.


  • 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.


  • 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

How to


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