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Difference between revisions of "Category:Machine Learning"
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− | + | This section describes the methodology used for force-field generation using machine learning. One first should checkout the theoretical background {{TAG|Machine learning force field: Theory}} 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}}. | |
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+ | == Input == | ||
+ | *Besides the usual input files ({{TAG|INCAR}}, {{TAG|POSCAR}}, etc.) the machine learning force field method requires the following input files: | ||
+ | **{{TAG|ML_AB}} Ab initio data used to create the training data. | ||
+ | **{{TAG|ML_FF}} File containing force field parameters. | ||
+ | == Output == | ||
+ | *The machine learning force field method generates the following output files: | ||
+ | **{{TAG|ML_LOGFILE}} Main output file for the machine learning force field method. | ||
+ | **{{TAG|ML_ABN}} New abinitio data (used as {{TAG|ML_AB}} in the next run). | ||
+ | **{{TAG|ML_REG}} Output file summarizing regression results. | ||
+ | **{{TAG|ML_HIS}} Output file summarizing the histogram data. | ||
+ | **{{TAG|ML_FFN}} File containing new force field parameters (used as {{TAG|ML_FF}} in the next run). | ||
+ | **{{TAG|ML_EATOM}} Output file containing local atomic energies | ||
+ | **{{TAG|ML_HEAT}} Output file including local heat flux | ||
+ | All {{TAG|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 == | ||
+ | *{{TAG|Machine learning force field: Theory}}. | ||
+ | == How to == | ||
+ | *{{TAG|Machine learning force field calculations: Basics}}. | ||
+ | *{{TAG|Machine learning force field calculations: Intermediate}}. | ||
+ | == Tutorial == | ||
+ | *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: | + | [[Category:VASP|Machine Learning]][[Category:Alpha]] |
Latest revision as of 15:02, 1 September 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.
Input
- Besides the usual input files (INCAR, POSCAR, etc.) the machine learning force field method requires the following input files:
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_ABN New abinitio data (used as ML_AB in the next run).
- ML_REG Output file summarizing regression results.
- ML_HIS Output file summarizing the histogram data.
- ML_FFN File containing new force field parameters (used as ML_FF 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
- Machine learning force field calculations: Basics.
- Machine learning force field calculations: Intermediate.
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.
Pages in category "Machine Learning"
The following 72 pages are in this category, out of 72 total.
M
- Machine learning force field calculations: Basics
- Machine learning force field calculations: Intermediate
- Machine learning force field: Theory
- ML AB
- ML ABN
- ML EATOM
- ML FF
- ML FF AFILT2 MB
- ML FF CDOUB
- ML FF CSF
- ML FF CSIG
- ML FF CSLOPE
- ML FF CTIFOR
- ML FF EATOM
- ML FF EPS LOW
- ML FF IAFILT2 MB
- ML FF IBROAD1 MB
- ML FF IBROAD2 MB
- ML FF ICOUPLE MB
- ML FF ICUT1 MB
- ML FF ICUT2 MB
- ML FF IERR
- ML FF IREG MB
- ML FF ISAMPLE
- ML FF ISCALE TOTEN MB
- ML FF ISOAP1 MB
- ML FF ISOAP2 MB
- ML FF ISTART
- ML FF IWEIGHT
- ML FF LAFILT2 MB
- ML FF LBASIS DISCARD
- ML FF LCOUPLE MB
- ML FF LCRITERIA
- ML FF LEATOM MB
- ML FF LHEAT MB
- ML FF LMAX2 MB
- ML FF LMLFF
- ML FF LNORM1 MB
- ML FF LNORM2 MB
- ML FF MB MB
- ML FF MCONF
- ML FF MCONF NEW
- ML FF MHIS
- ML FF MRB1 MB
- ML FF MRB2 MB
- ML FF MSPL1 MB
- ML FF MSPL2 MB
- ML FF NATOM COUPLED MB
- ML FF NDIM SCALAPACK
- ML FF NHYP1 MB
- ML FF NHYP2 MB
- ML FF NMDINT
- ML FF NR1 MB
- ML FF NR2 MB
- ML FF NWRITE
- ML FF RCOUPLE MB
- ML FF RCUT1 MB
- ML FF RCUT2 MB
- ML FF SIGV0 MB
- ML FF SIGW0 MB
- ML FF SION1 MB
- ML FF SION2 MB
- ML FF W1 MB
- ML FF WTIFOR
- ML FF WTOTEN
- ML FF WTSIF
- ML FF XMIX
- ML FFN
- ML HEAT
- ML HIS
- ML LOGFILE
- ML REG