Requests for technical support from the VASP group should be posted in the VASP-forum.

Difference between revisions of "Category:Machine Learning"

From Vaspwiki
Jump to navigationJump to search
m (Reverted edits by Schlipf (talk) to last revision by Karsai)
Line 1: Line 1:
This section describes how to use machine learning to generate force fields. First, one should become familiar with the theoretical background {{TAG|On-the-fly machine learning force field generation using Bayesian linear regression}}. Secondly, one can focus on how to set up and run the machine learning calculations within VASP {{TAG|Machine learning force field calculations: Basics}}. Finally, one gains some hands-on experience with the following tutorial: {{TAG|Liquid Si - MLFF}}.
+
This section describes the methodology used for force-field generation using machine learning. One first should checkout the theoretical background {{TAG|On-the-fly machine learning force field generation using Bayesian linear regression}} together with the basic description how to run the machine learning calculations {{TAG|Machine learning force field calculations: Basics}}. Then gain some hands-on experience with the following tutorial: {{TAG|Liquid Si - MLFF}}.
  
 
== Input ==
 
== Input ==
Line 29: Line 29:
  
 
== Tutorial ==
 
== Tutorial ==
*Basic tutorial on how to perform on-the-fly learning and how to control the accuracy of the force field: {{TAG|Liquid Si - MLFF}}.
+
*Basic tutorial to learn how to perform on-the-fly learning and how to control the accuracy of the force field: {{TAG|Liquid Si - MLFF}}.
  
 
----
 
----
 
[[Category:VASP|Machine Learning]][[Category:VASP6]]
 
[[Category:VASP|Machine Learning]][[Category:VASP6]]

Revision as of 13:44, 2 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.