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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:
- 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.
- Machine learning force field calculations: Basics.
- Machine learning force field calculations: Intermediate.
- Basic tutorial to learn how to perform on-the-fly learning and how to control the accuracy of the force field: Liquid Si - MLFF.
This category has only the following subcategory.
Pages in category "Machine Learning"
The following 73 pages are in this category, out of 73 total.