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Difference between revisions of "Category:Machine Learning"

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== Theoretical Background ==
+
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}}.
*{{TAG|On-the-fly machine learning force field generation using Bayesian linear regression}}.
 
  
 +
== Input ==
  
{{TAG| ML_FF_LMLFF }}
+
*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.
  
{{TAG| ML_FF_CDOUB }}
+
== Output ==
  
{{TAG| ML_FF_CHALF }}
+
*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
  
{{TAG| ML_FF_CSF }}
 
  
{{TAG| ML_FF_CSIG }}
 
  
{{TAG| ML_FF_CSLOPE }}
+
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''.
  
{{TAG| ML_FF_CTIFOR }}
+
== Theoretical Background ==
 
+
*{{TAG|Machine learning force field: Theory}}.
{{TAG| ML_FF_EATOM }}
 
 
 
{{TAG| ML_FF_IERR }}
 
 
 
{{TAG| ML_FF_IMAT_SPARS }}
 
 
 
{{TAG| ML_FF_ISAMPLE }}
 
 
 
{{TAG| ML_FF_ISTART }}
 
 
 
{{TAG| ML_FF_ISVD }}
 
 
 
{{TAG| ML_FF_LBASIS_DISCARD }}
 
 
 
{{TAG| ML_FF_LCONF_DISCARD }}
 
 
 
{{TAG| ML_FF_LCRITERIA }}
 
 
 
{{TAG| ML_FF_LMLMB }}
 
 
 
{{TAG| ML_FF_LMLPC }}
 
 
 
{{TAG| ML_FF_LERR }}
 
 
 
{{TAG| ML_FF_LMODEL }}
 
 
 
{{TAG| ML_FF_LMODEL_ONLY }}
 
 
 
{{TAG| ML_FF_NDIM_SCALAPACK }}
 
 
 
{{TAG| ML_FF_NWRITE }}
 
 
 
{{TAG| ML_FF_ITYPE_MODEL }}
 
 
 
{{TAG| ML_FF_EPSILON_LJ_MODEL }}
 
 
 
{{TAG| ML_FF_RCUT_LJ_MODEL }}
 
 
 
{{TAG| ML_FF_SIGMA_LJ_MODEL }}
 
 
 
{{TAG| ML_FF_EPSILON_LJ_MODEL }}
 
 
 
{{TAG| ML_FF_RCUT_LJ_MODEL }}
 
 
 
{{TAG| ML_FF_SIGMA_LJ_MODEL }}
 
 
 
{{TAG| ML_FF_V_AT_MODEL }}
 
 
 
{{TAG| ML_FF_EPSILON_SW_MODEL }}
 
 
 
{{TAG| ML_FF_SIGMA_SW_MODEL }}
 
 
 
{{TAG| ML_FF_IWEIGHT }}
 
 
 
{{TAG| ML_FF_LTEST }}
 
 
 
{{TAG| ML_FF_LTRJ }}
 
 
 
{{TAG| ML_FF_MCONF }}
 
 
 
{{TAG| ML_FF_MCONF_NEW }}
 
 
 
{{TAG| ML_FF_MHIS }}
 
 
 
{{TAG| ML_FF_NMDINT }}
 
 
 
{{TAG| ML_FF_NTEST }}
 
 
 
{{TAG| ML_FF_WQION }}
 
 
 
{{TAG| ML_FF_WTIFOR }}
 
 
 
{{TAG| ML_FF_WTOTEN }}
 
 
 
{{TAG| ML_FF_WTSIF }}
 
 
 
{{TAG| ML_FF_XMIX }}
 
 
 
{{TAG| ML_FF_ISOAP1_MB }}
 
 
 
{{TAG| ML_FF_ISOAP2_MB }}
 
 
 
{{TAG| ML_FF_ELEMENT_HYP1_MB }}
 
 
 
{{TAG| ML_FF_ELEMENT_HYP2_MB }}
 
 
 
{{TAG| ML_FF_LAFILT2_MB }}
 
 
 
{{TAG| ML_FF_IAFILT2_MB }}
 
 
 
{{TAG| ML_FF_AFILT2_MB }}
 
 
 
{{TAG| ML_FF_IBROAD1_MB }}
 
 
 
{{TAG| ML_FF_IBROAD2_MB }}
 
 
 
{{TAG| ML_FF_ICUT1_MB }}
 
 
 
{{TAG| ML_FF_ICUT2_MB }}
 
 
 
{{TAG| ML_FF_INVERSE_SOAP_MB }}
 
 
 
{{TAG| ML_FF_IPHYS1_MB }}
 
 
 
{{TAG| ML_FF_IPHYS2_MB }}
 
 
 
{{TAG| ML_FF_IREG_MB }}
 
 
 
{{TAG| ML_FF_ISCALE_TOTEN_MB }}
 
 
 
{{TAG| ML_FF_LEATOM_MB }}
 
 
 
{{TAG| ML_FF_LHEAT_MB }}
 
 
 
{{TAG| ML_FF_LMETRIC1_MB }}
 
 
 
{{TAG| ML_FF_LMETRIC2_MB }}
 
 
 
{{TAG| ML_FF_LVARTRAN1_MB }}
 
 
 
{{TAG| ML_FF_LVARTRAN2_MB }}
 
 
 
{{TAG| ML_FF_NMETRIC1_MB }}
 
 
 
{{TAG| ML_FF_NMETRIC2_MB }}
 
 
 
{{TAG| ML_FF_NVARTRAN1_MB }}
 
 
 
{{TAG| ML_FF_NVARTRAN2_MB }}
 
 
 
{{TAG| ML_FF_LWINDOW1_MB }}
 
 
 
{{TAG| ML_FF_LWINDOW2_MB }}
 
 
 
{{TAG| ML_FF_IWINDOW1_MB }}
 
 
 
{{TAG| ML_FF_IWINDOW2_MB }}
 
 
 
{{TAG| ML_FF_LMAX2_MB }}
 
 
 
{{TAG| ML_FF_LNORM1_MB }}
 
 
 
{{TAG| ML_FF_LNORM2_MB }}
 
 
 
{{TAG| ML_FF_MB_MB }}
 
 
 
{{TAG| ML_FF_MSPL1_MB }}
 
 
 
{{TAG| ML_FF_MSPL2_MB }}
 
 
 
{{TAG| ML_FF_NHYP1_MB }}
 
 
 
{{TAG| ML_FF_NHYP2_MB }}
 
 
 
{{TAG| ML_FF_NR1_MB }}
 
 
 
{{TAG| ML_FF_NR2_MB }}
 
 
 
{{TAG| ML_FF_MRB2_MB }}
 
 
 
{{TAG| ML_FF_MRB1_MB }}
 
 
 
{{TAG| ML_FF_RCUT2_MB }}
 
 
 
{{TAG| ML_FF_RCUT1_MB }}
 
 
 
{{TAG| ML_FF_RMETRIC1_MB }}
 
 
 
{{TAG| ML_FF_RMETRIC2_MB }}
 
 
 
{{TAG| ML_FF_SIGV0_MB }}
 
 
 
{{TAG| ML_FF_SIGW0_MB }}
 
 
 
{{TAG| ML_FF_W1_MB }}
 
 
 
{{TAG| ML_FF_W2_MB }}
 
 
 
{{TAG| ML_FF_SION1_MB }}
 
 
 
{{TAG| ML_FF_SION2_MB }}
 
 
 
{{TAG| ML_FF_LCOUPLE_MB }}
 
 
 
{{TAG| ML_FF_NATOM_COUPLED_MB }}
 
 
 
{{TAG| ML_FF_ICOUPLE_MB }}
 
 
 
{{TAG| ML_FF_RCOUPLE_MB }}
 
 
 
{{TAG| ML_FF_ICUT_Q }}
 
 
 
{{TAG| ML_FF_ICHG_Q }}
 
 
 
{{TAG| ML_FF_IREG_Q }}
 
 
 
{{TAG| ML_FF_ISCALE_QION_Q }}
 
 
 
{{TAG| ML_FF_NR_Q }}
 
 
 
{{TAG| ML_FF_MRB_Q }}
 
 
 
{{TAG| ML_FF_RCUT_Q }}
 
 
 
{{TAG| ML_FF_RCUTSR_Q }}
 
 
 
{{TAG| ML_FF_RCUTEW_Q }}
 
 
 
{{TAG| ML_FF_SIGV0_Q }}
 
 
 
{{TAG| ML_FF_SIGW0_Q }}
 
 
 
{{TAG| ML_FF_EPSEW_Q }}
 
 
 
{{TAG| ML_FF_LWINDOW_Q }}
 
 
 
{{TAG| ML_FF_IWINDOW_Q }}
 
  
 +
== 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:VASP6]]
+
[[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:
    • ML_AB Ab initio data used to create the training data.
    • ML_FF 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_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

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.