Category:Machine-learned force fields - Revision history

Csheldon: /* Tutorials */

2025-05-28T13:27:05Z

Tutorials

← Older revision		Revision as of 13:27, 28 May 2025
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	== Tutorials ==		== Tutorials ==
	*Tutorial for [https://vasp.at/tutorials/latest/mlff/part1/ ~~MLFF calculations~~].		*Tutorial for [https://vasp.at/tutorials/latest/md/part2/ training a machine learning force field using MD].
			*Tutorial for [https://vasp.at/tutorials/latest/mlff/part1/ error analysis and hyperparameter optimization].


	[[Category:VASP\|Machine-learned force fields]][[Category:Ionic minimization\|Ionic minimization]][[Category:Molecular dynamics\|Molecular dynamics]][[Category:Forces\|Forces]]		[[Category:VASP\|Machine-learned force fields]][[Category:Ionic minimization\|Ionic minimization]][[Category:Molecular dynamics\|Molecular dynamics]][[Category:Forces\|Forces]]

Csheldon at 13:23, 28 May 2025

2025-05-28T13:23:27Z

← Older revision		Revision as of 13:23, 28 May 2025
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	, threshold and the total number of descriptors in the sparsification ({{TAG\|ML_EPS_LOW}}, {{TAG\|ML_RDES_SPARSDES}}). We recommend optimizing these in cross-validation by means of systematically varying the parameters and refitting the force field ({{TAG\|ML_MODE}}=refit). In principle, it is necessary to do that for all hyperparameters available in VASP to obtain the best MLFF model. In practice, usually the default values are a good guess. The default parameters of VASP were optimized on a selected set of bulk materials and on a molecular data base.		, threshold and the total number of descriptors in the sparsification ({{TAG\|ML_EPS_LOW}}, {{TAG\|ML_RDES_SPARSDES}}). We recommend optimizing these in cross-validation by means of systematically varying the parameters and refitting the force field ({{TAG\|ML_MODE}}=refit). In principle, it is necessary to do that for all hyperparameters available in VASP to obtain the best MLFF model. In practice, usually the default values are a good guess. The default parameters of VASP were optimized on a selected set of bulk materials and on a molecular data base.

	~~----~~		== Tutorials ==
			*Tutorial for [https://vasp.at/tutorials/latest/mlff/part1/ MLFF calculations].

	[[Category:VASP\|Machine-learned force fields]][[Category:Ionic minimization\|Ionic minimization]][[Category:Molecular dynamics\|Molecular dynamics]][[Category:Forces\|Forces]]		[[Category:VASP\|Machine-learned force fields]][[Category:Ionic minimization\|Ionic minimization]][[Category:Molecular dynamics\|Molecular dynamics]][[Category:Forces\|Forces]]

Singraber: /* How to */

2025-01-24T08:37:40Z

How to

← Older revision		Revision as of 08:37, 24 January 2025
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	*[https://www.vasp.at/tutorials/latest/md/part2/ Basic tutorial that provides hands-on experience for silicon].		*[https://www.vasp.at/tutorials/latest/md/part2/ Basic tutorial that provides hands-on experience for silicon].
	*{{TAG\|Liquid Si - MLFF}}.		*{{TAG\|Liquid Si - MLFF}}.
			*[[Running machine-learned force fields in LAMMPS]]

	=== Input ===		=== Input ===

Huebsch: /* Hyperparameters */

2023-10-04T09:31:11Z

Hyperparameters

← Older revision		Revision as of 09:31, 4 October 2023
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	Hyperparameters are user-defined parameters of the MLFF model that will not be optimized during training of the MLFF. For instance, cutoff radii ({{TAG\|ML_RCUT1}}, {{TAG\|ML_RCUT2}}), the normalization and weighting of ab-initio training data ({{TAG\|ML_IWEIGHT}}, {{TAG\|ML_WTOTEN}}, {{TAG\|ML_WTIFOR}}, {{TAG\|ML_WTSIF}})		Hyperparameters are user-defined parameters of the MLFF model that will not be optimized during training of the MLFF. For instance, cutoff radii ({{TAG\|ML_RCUT1}}, {{TAG\|ML_RCUT2}}), the normalization and weighting of ab-initio training data ({{TAG\|ML_IWEIGHT}}, {{TAG\|ML_WTOTEN}}, {{TAG\|ML_WTIFOR}}, {{TAG\|ML_WTSIF}})
	, threshold and the total number of descriptors in the sparsification ({{TAG\|ML_EPS_LOW}}, {{TAG\|ML_RDES_SPARSDES}}). We recommend optimizing these in cross-validation by means of systematically varying the parameters and refitting the force field ({{TAG\|ML_MODE}}=refit).		, threshold and the total number of descriptors in the sparsification ({{TAG\|ML_EPS_LOW}}, {{TAG\|ML_RDES_SPARSDES}}). We recommend optimizing these in cross-validation by means of systematically varying the parameters and refitting the force field ({{TAG\|ML_MODE}}=refit). In principle, it is necessary to do that for all hyperparameters available in VASP to obtain the best MLFF model. In practice, usually the default values are a good guess. The default parameters of VASP were optimized on a selected set of bulk materials and on a molecular data base.

	----		----
	[[Category:VASP\|Machine-learned force fields]][[Category:Ionic minimization\|Ionic minimization]][[Category:Molecular dynamics\|Molecular dynamics]][[Category:Forces\|Forces]]		[[Category:VASP\|Machine-learned force fields]][[Category:Ionic minimization\|Ionic minimization]][[Category:Molecular dynamics\|Molecular dynamics]][[Category:Forces\|Forces]]

Huebsch: /* Hyperparameters */

2023-10-03T07:39:00Z

Hyperparameters

← Older revision		Revision as of 07:39, 3 October 2023
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	=== Hyperparameters ===		=== Hyperparameters ===

	Hyperparameters are user-defined parameters of the MLFF model that will not be optimized during training of the MLFF. For instance, {{TAG\|ML_RCUT1}}, {{TAG\|ML_RCUT2}}, {{TAG\|~~ML_WTIFOR~~}}, {{TAG\|ML_WTOTEN}}, {{TAG\|ML_WTSIF}}, {{TAG\|ML_EPS_LOW}}, {{TAG\|ML_RDES_SPARSDES}}. We recommend optimizing these in cross-validation by means of systematically varying the parameters and refitting the force field ({{TAG\|ML_MODE}}=refit).		Hyperparameters are user-defined parameters of the MLFF model that will not be optimized during training of the MLFF. For instance, cutoff radii ({{TAG\|ML_RCUT1}}, {{TAG\|ML_RCUT2}}), the normalization and weighting of ab-initio training data ({{TAG\|ML_IWEIGHT}}, {{TAG\|ML_WTOTEN}}, {{TAG\|ML_WTIFOR}}, {{TAG\|ML_WTSIF}})
			, threshold and the total number of descriptors in the sparsification ({{TAG\|ML_EPS_LOW}}, {{TAG\|ML_RDES_SPARSDES}}). We recommend optimizing these in cross-validation by means of systematically varying the parameters and refitting the force field ({{TAG\|ML_MODE}}=refit).

	----		----
	[[Category:VASP\|Machine-learned force fields]][[Category:Ionic minimization\|Ionic minimization]][[Category:Molecular dynamics\|Molecular dynamics]][[Category:Forces\|Forces]]		[[Category:VASP\|Machine-learned force fields]][[Category:Ionic minimization\|Ionic minimization]][[Category:Molecular dynamics\|Molecular dynamics]][[Category:Forces\|Forces]]

Huebsch: /* How to */

2023-10-03T06:32:27Z

How to

← Older revision		Revision as of 06:32, 3 October 2023
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	*{{TAG\|ML_EATOM}} Local atomic energies.		*{{TAG\|ML_EATOM}} Local atomic energies.
	*{{TAG\|ML_HEAT}} Local heat flux.		*{{TAG\|ML_HEAT}} Local heat flux.

			=== Hyperparameters ===

			Hyperparameters are user-defined parameters of the MLFF model that will not be optimized during training of the MLFF. For instance, {{TAG\|ML_RCUT1}}, {{TAG\|ML_RCUT2}}, {{TAG\|ML_WTIFOR}}, {{TAG\|ML_WTOTEN}}, {{TAG\|ML_WTSIF}}, {{TAG\|ML_EPS_LOW}}, {{TAG\|ML_RDES_SPARSDES}}. We recommend optimizing these in cross-validation by means of systematically varying the parameters and refitting the force field ({{TAG\|ML_MODE}}=refit).

	----		----
	[[Category:VASP\|Machine-learned force fields]][[Category:Ionic minimization\|Ionic minimization]][[Category:Molecular dynamics\|Molecular dynamics]][[Category:Forces\|Forces]]		[[Category:VASP\|Machine-learned force fields]][[Category:Ionic minimization\|Ionic minimization]][[Category:Molecular dynamics\|Molecular dynamics]][[Category:Forces\|Forces]]

Karsai: /* Input */

2023-03-29T07:31:41Z

Input

← Older revision		Revision as of 07:31, 29 March 2023
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	=== Input ===		=== Input ===

	Depending on whether the calculation is training, retraining, or applying the force field (see {{TAG\|~~ML_ISTART~~}}), VASP may require the following input files in addition to the usual [[:Category:Input files\|input files]] ({{TAG\|INCAR}}, {{TAG\|POSCAR}}, etc.):		Depending on whether the calculation is training, retraining, or applying the force field (see {{TAG\|ML_MODE}}), VASP may require the following input files in addition to the usual [[:Category:Input files\|input files]] ({{TAG\|INCAR}}, {{TAG\|POSCAR}}, etc.):
	*{{TAG\|ML_AB}} Ab-initio training data.		*{{TAG\|ML_AB}} Ab-initio training data.
	*{{TAG\|ML_FF}} Force-field parameters.		*{{TAG\|ML_FF}} Force-field parameters.

Karsai: /* Output */

2022-07-20T12:42:10Z

Output

← Older revision		Revision as of 12:42, 20 July 2022
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	----		----
	[[Category:VASP\|Machine-learned force fields]][[Category:Ionic minimization\|Ionic minimization]][[Category:Molecular dynamics\|Molecular dynamics]][[Category:Forces\|Forces]]		[[Category:VASP\|Machine-learned force fields]][[Category:Ionic minimization\|Ionic minimization]][[Category:Molecular dynamics\|Molecular dynamics]][[Category:Forces\|Forces]]

	~~{{TAG\|ML_LBASIS_DISCARD}}~~

Karsai: /* Output */

2022-07-20T12:42:01Z

Output

← Older revision		Revision as of 12:42, 20 July 2022
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	----		----
	[[Category:VASP\|Machine-learned force fields]][[Category:Ionic minimization\|Ionic minimization]][[Category:Molecular dynamics\|Molecular dynamics]][[Category:Forces\|Forces]]		[[Category:VASP\|Machine-learned force fields]][[Category:Ionic minimization\|Ionic minimization]][[Category:Molecular dynamics\|Molecular dynamics]][[Category:Forces\|Forces]]

			{{TAG\|ML_LBASIS_DISCARD}}

Miranda.henrique at 15:14, 19 July 2022

2022-07-19T15:14:51Z

← Older revision		Revision as of 15:14, 19 July 2022
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	=== Input ===		=== Input ===

	:Depending on whether the calculation is training, retraining, or applying the force field (see {{TAG\|ML_ISTART}}), VASP may require the following input files in addition to the usual [[:Category:Input files\|input files]] ({{TAG\|INCAR}}, {{TAG\|POSCAR}}, etc.):		Depending on whether the calculation is training, retraining, or applying the force field (see {{TAG\|ML_ISTART}}), VASP may require the following input files in addition to the usual [[:Category:Input files\|input files]] ({{TAG\|INCAR}}, {{TAG\|POSCAR}}, etc.):
	:*{{TAG\|ML_AB}} Ab-initio training data.		*{{TAG\|ML_AB}} Ab-initio training data.
	:*{{TAG\|ML_FF}} Force-field parameters.		*{{TAG\|ML_FF}} Force-field parameters.

	=== Output ===		=== Output ===

	:The machine-learning–force-field method generates the following [[:Category:Output files\|output files]]:		The machine-learning–force-field method generates the following [[:Category:Output files\|output files]]:
	**{{TAG\|ML_LOGFILE}} Main output file.		*{{TAG\|ML_LOGFILE}} Main output file.
	**{{TAG\|ML_ABN}} Generated ab-initio training data. It is used as {{FILE\|ML_AB}} file to restart a calculation.		*{{TAG\|ML_ABN}} Generated ab-initio training data. It is used as {{FILE\|ML_AB}} file to restart a calculation.
	**{{TAG\|ML_REG}} Summary of regression results.		*{{TAG\|ML_REG}} Summary of regression results.
	**{{TAG\|ML_HIS}} Summary of the histogram data.		*{{TAG\|ML_HIS}} Summary of the histogram data.
	**{{TAG\|ML_FFN}} Force-field parameters. It is used as {{FILE\|ML_FF}} file to restart a calculation.		*{{TAG\|ML_FFN}} Force-field parameters. It is used as {{FILE\|ML_FF}} file to restart a calculation.
	**{{TAG\|ML_EATOM}} Local atomic energies.		*{{TAG\|ML_EATOM}} Local atomic energies.
	**{{TAG\|ML_HEAT}} Local heat flux.		*{{TAG\|ML_HEAT}} Local heat flux.

	----		----
	[[Category:VASP\|Machine-learned force fields]][[Category:Ionic minimization\|Ionic minimization]][[Category:Molecular dynamics\|Molecular dynamics]][[Category:Forces\|Forces]]		[[Category:VASP\|Machine-learned force fields]][[Category:Ionic minimization\|Ionic minimization]][[Category:Molecular dynamics\|Molecular dynamics]][[Category:Forces\|Forces]]