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Difference between revisions of "Category:Machine Learning"

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== Theoretical Background ==
 
== Theoretical Background ==
*{{TAG|On-the-fly machine learning force field generation using Bayesian linear regression}}.
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*{{TAG|Machine learning force field: Theory}}.
  
 
== How to ==
 
== How to ==

Revision as of 13:23, 5 September 2019

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. 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_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

How to

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.