ML MODE - Revision history

Karsai at 09:44, 20 March 2026

2026-03-20T09:44:29Z

← Older revision		Revision as of 09:44, 20 March 2026
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	Optionally, if the force field was refitted using {{TAG\|ML_MODE\|refitbayesian}}, the Bayesian error estimate of the energies, forces, and stress can be computed and logged in the {{FILE\|ML_LOGFILE}}. The output frequency of the Bayesian errors can be set via the {{TAG\|ML_IERR}} tag, the default is 0.</p><p>		Optionally, if the force field was refitted using {{TAG\|ML_MODE\|refitbayesian}}, the Bayesian error estimate of the energies, forces, and stress can be computed and logged in the {{FILE\|ML_LOGFILE}}. The output frequency of the Bayesian errors can be set via the {{TAG\|ML_IERR}} tag, the default is 0.</p><p>
	For VASP versions prior to 6.4.0 this corresponds to {{TAG\|ML_ISTART\|2}}.</p>		For VASP versions prior to 6.4.0 this corresponds to {{TAG\|ML_ISTART\|2}}.</p>
			<li>{{TAG\|ML_MODE\|delta}}''': Performs ab-initio calcutions and force field predictions'''<p>
			A previously trained machine learning force field is read from the {{FILE\|ML_FF}} file, and the MD simulation is driven by the sum of ''ab initio'' forces and machine learning predictions. The machine learning force field ideally has been trained to predict the difference between two levels of ''ab initio'' theory — referred to as the target and reference levels — so that the combined result approximates the target level of theory at the computational cost of the reference level.
			Typical use cases include correcting a low-cost exchange-correlation functional (e.g., PBE) toward a higher-accuracy one (e.g., a hybrid functional such as HSE06), or bridging differences in k-mesh density, plane-wave energy cutoff, or other convergence parameters. In this way, near-hybrid accuracy can be achieved at a cost approaching that of a semi-local DFT calculation.</p><p>
			This mode is available for VASP version 6.6.0 or higher.</p>
	</li><br />		</li><br />
	<li>{{TAG\|ML_MODE\|none}}''': The tag is ignored'''		<li>{{TAG\|ML_MODE\|none}}''': The tag is ignored'''

Karsai at 09:19, 20 March 2026

2026-03-20T09:19:17Z

← Older revision		Revision as of 09:19, 20 March 2026
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	{{DISPLAYTITLE:ML_MODE}}		{{DISPLAYTITLE:ML_MODE}}
	{{TAGDEF\|ML_MODE\|train {{!}} select {{!}} refit {{!}} refitbayesian {{!}} run {{!}} none \| none}}		{{TAGDEF\|ML_MODE\|train {{!}} select {{!}} refit {{!}} refitbayesian {{!}} run {{!}} delta {{!}} none \| none}}

	Description: String-based tag selecting operation mode for machine learning force fields.		Description: String-based tag selecting operation mode for machine learning force fields.

Singraber at 08:02, 24 October 2025

2025-10-24T08:02:26Z

Karsai at 14:43, 20 August 2024

2024-08-20T14:43:50Z

← Older revision		Revision as of 14:43, 20 August 2024
Line 32:		Line 32:
	The {{FILE\|ML_AB}} file may contain values for <tt>CTIFOR</tt> for each training structure. These are the thresholds used to sample that structure from the previous training. The thresholds found on the {{FILE\|ML_AB}} will be re-used unless a threshold is explicitly specified in the {{FILE\|INCAR}} file, by means of the {{TAG\|ML_CTIFOR}} tag. In the latter case the thresholds from the {{TAG\|ML_AB}} file are ignored. In case the {{FILE\|ML_AB}} contains '''no''' <tt>CTIFOR</tt> information and '''no''' threshold is specified in the {{FILE\|INCAR}} file, the default value for {{TAG\|ML_CTIFOR}} is used.</p><p>		The {{FILE\|ML_AB}} file may contain values for <tt>CTIFOR</tt> for each training structure. These are the thresholds used to sample that structure from the previous training. The thresholds found on the {{FILE\|ML_AB}} will be re-used unless a threshold is explicitly specified in the {{FILE\|INCAR}} file, by means of the {{TAG\|ML_CTIFOR}} tag. In the latter case the thresholds from the {{TAG\|ML_AB}} file are ignored. In case the {{FILE\|ML_AB}} contains '''no''' <tt>CTIFOR</tt> information and '''no''' threshold is specified in the {{FILE\|INCAR}} file, the default value for {{TAG\|ML_CTIFOR}} is used.</p><p>
	This mode automatically sets {{TAGO\|NSW\|1}} and {{TAGO\|ML_CDOUB\|4}}.For VASP versions prior to 6.4.0 this corresponds to {{TAGO\|ML_ISTART\|3}}.		This mode automatically sets {{TAGO\|NSW\|1}} and {{TAGO\|ML_CDOUB\|4}}.For VASP versions prior to 6.4.0 this corresponds to {{TAGO\|ML_ISTART\|3}}.
	{{NB\|warning\|{{TAGO\|ML_MODE\|select}} ignores the {{TAG\|POSCAR}} and hence no ~~prediction is~~ made at the end of this calculation (instead zeros are written to stdout, {{TAG\|OSZICAR}} and {{TAG\|OUTCAR}}).}}</p>		{{NB\|warning\|{{TAGO\|ML_MODE\|select}} ignores the structure in the {{TAG\|POSCAR}} and hence no error, force and stress predictions are made at the end of this calculation (instead zeros are written to stdout, {{TAG\|OSZICAR}} and {{TAG\|OUTCAR}}).}}</p>
	</li><br />		</li><br />
	<li>{{TAGO\|ML_MODE\|refit}}''': Refit a force field for "fast" evaluation'''<p>		<li>{{TAGO\|ML_MODE\|refit}}''': Refit a force field for "fast" evaluation'''<p>

Karsai at 14:41, 20 August 2024

2024-08-20T14:41:58Z

← Older revision		Revision as of 14:41, 20 August 2024
Line 32:		Line 32:
	The {{FILE\|ML_AB}} file may contain values for <tt>CTIFOR</tt> for each training structure. These are the thresholds used to sample that structure from the previous training. The thresholds found on the {{FILE\|ML_AB}} will be re-used unless a threshold is explicitly specified in the {{FILE\|INCAR}} file, by means of the {{TAG\|ML_CTIFOR}} tag. In the latter case the thresholds from the {{TAG\|ML_AB}} file are ignored. In case the {{FILE\|ML_AB}} contains '''no''' <tt>CTIFOR</tt> information and '''no''' threshold is specified in the {{FILE\|INCAR}} file, the default value for {{TAG\|ML_CTIFOR}} is used.</p><p>		The {{FILE\|ML_AB}} file may contain values for <tt>CTIFOR</tt> for each training structure. These are the thresholds used to sample that structure from the previous training. The thresholds found on the {{FILE\|ML_AB}} will be re-used unless a threshold is explicitly specified in the {{FILE\|INCAR}} file, by means of the {{TAG\|ML_CTIFOR}} tag. In the latter case the thresholds from the {{TAG\|ML_AB}} file are ignored. In case the {{FILE\|ML_AB}} contains '''no''' <tt>CTIFOR</tt> information and '''no''' threshold is specified in the {{FILE\|INCAR}} file, the default value for {{TAG\|ML_CTIFOR}} is used.</p><p>
	This mode automatically sets {{TAGO\|NSW\|1}} and {{TAGO\|ML_CDOUB\|4}}.For VASP versions prior to 6.4.0 this corresponds to {{TAGO\|ML_ISTART\|3}}.		This mode automatically sets {{TAGO\|NSW\|1}} and {{TAGO\|ML_CDOUB\|4}}.For VASP versions prior to 6.4.0 this corresponds to {{TAGO\|ML_ISTART\|3}}.
	{{NB\|warning\|{{TAGO\|ML_MODE\|select}} ignores the {{TAG\|POSCAR}} and hence no prediction is made at the end of this calculation (instead zeros are written to stdout, {{TAG\|OSZICAR}} and {{TAG\|OUTCAR}}.}}</p>		{{NB\|warning\|{{TAGO\|ML_MODE\|select}} ignores the {{TAG\|POSCAR}} and hence no prediction is made at the end of this calculation (instead zeros are written to stdout, {{TAG\|OSZICAR}} and {{TAG\|OUTCAR}}).}}</p>
	</li><br />		</li><br />
	<li>{{TAGO\|ML_MODE\|refit}}''': Refit a force field for "fast" evaluation'''<p>		<li>{{TAGO\|ML_MODE\|refit}}''': Refit a force field for "fast" evaluation'''<p>

Karsai at 14:41, 20 August 2024

2024-08-20T14:41:33Z

← Older revision		Revision as of 14:41, 20 August 2024
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	{{NB\|tip\|If calculations for {{TAGO\|ML_MODE\|select}} are too time-consuming, it is useful to increase {{TAG\|ML_MCONF_NEW}} to values around 10-16. Together with {{TAGO\|ML_CDOUB\|4}}, this often accelerates the calculations by a factor of 2-4.}}<p>		{{NB\|tip\|If calculations for {{TAGO\|ML_MODE\|select}} are too time-consuming, it is useful to increase {{TAG\|ML_MCONF_NEW}} to values around 10-16. Together with {{TAGO\|ML_CDOUB\|4}}, this often accelerates the calculations by a factor of 2-4.}}<p>
	The {{FILE\|ML_AB}} file may contain values for <tt>CTIFOR</tt> for each training structure. These are the thresholds used to sample that structure from the previous training. The thresholds found on the {{FILE\|ML_AB}} will be re-used unless a threshold is explicitly specified in the {{FILE\|INCAR}} file, by means of the {{TAG\|ML_CTIFOR}} tag. In the latter case the thresholds from the {{TAG\|ML_AB}} file are ignored. In case the {{FILE\|ML_AB}} contains '''no''' <tt>CTIFOR</tt> information and '''no''' threshold is specified in the {{FILE\|INCAR}} file, the default value for {{TAG\|ML_CTIFOR}} is used.</p><p>		The {{FILE\|ML_AB}} file may contain values for <tt>CTIFOR</tt> for each training structure. These are the thresholds used to sample that structure from the previous training. The thresholds found on the {{FILE\|ML_AB}} will be re-used unless a threshold is explicitly specified in the {{FILE\|INCAR}} file, by means of the {{TAG\|ML_CTIFOR}} tag. In the latter case the thresholds from the {{TAG\|ML_AB}} file are ignored. In case the {{FILE\|ML_AB}} contains '''no''' <tt>CTIFOR</tt> information and '''no''' threshold is specified in the {{FILE\|INCAR}} file, the default value for {{TAG\|ML_CTIFOR}} is used.</p><p>
	This mode automatically sets {{TAGO\|NSW\|1}} and {{TAGO\|ML_CDOUB\|4}}.For VASP versions prior to 6.4.0 this corresponds to {{TAGO\|ML_ISTART\|3}}.</p>		This mode automatically sets {{TAGO\|NSW\|1}} and {{TAGO\|ML_CDOUB\|4}}.For VASP versions prior to 6.4.0 this corresponds to {{TAGO\|ML_ISTART\|3}}.
			{{NB\|warning\|{{TAGO\|ML_MODE\|select}} ignores the {{TAG\|POSCAR}} and hence no prediction is made at the end of this calculation (instead zeros are written to stdout, {{TAG\|OSZICAR}} and {{TAG\|OUTCAR}}.}}</p>
	</li><br />		</li><br />
	<li>{{TAGO\|ML_MODE\|refit}}''': Refit a force field for "fast" evaluation'''<p>		<li>{{TAGO\|ML_MODE\|refit}}''': Refit a force field for "fast" evaluation'''<p>
	Similar to {{TAGO\|ML_MODE\|select}}, refitting is done based on an existing {{TAG\|ML_AB}} file, but the number of local reference configurations for each species is taken from the {{TAG\|ML_AB}} file. Sparsification is performed on the local reference configurations, so the resulting {{TAG\|ML_ABN}} file will contain the same number or fewer local reference configurations than the {{TAG\|ML_AB}} file.</p><p>		Similar to {{TAGO\|ML_MODE\|select}}, refitting is done based on an existing {{TAG\|ML_AB}} file, but the number of local reference configurations for each species is taken from the {{TAG\|ML_AB}} file. Sparsification is performed on the local reference configurations, so the resulting {{TAG\|ML_ABN}} file will contain the same number or fewer local reference configurations than the {{TAG\|ML_AB}} file.</p><p>
	By default the resulting force field is geared towards "fast" evaluation to speed up production runs ({{TAGO\|ML_LFAST\|.TRUE.}}). This comes at the cost of not being able to evaluate Bayesian error estimates.</p>		By default the resulting force field is geared towards "fast" evaluation to speed up production runs ({{TAGO\|ML_LFAST\|.TRUE.}}). This comes at the cost of not being able to evaluate Bayesian error estimates.</p>
	{{NB\|warning\|We strongly advise to use {{TAGO\|ML_MODE\|refit}} if no error estimates are required during production runs.}}<p>		{{NB\|warning\|We strongly advise to use {{TAGO\|ML_MODE\|refit}} if no Bayesian error estimates are required during production runs.}}<p>
	This mode automatically sets {{TAGO\|ML_LFAST\|.TRUE.}}, {{TAGO\|NSW\|1}}, {{TAGO\|ML_IALGO_LINREG\|4}}, {{TAGO\|ML_SIGW0\|1E-7}}, {{TAGO\|ML_SIGV0\|1}} and {{TAGO\|ML_EPS_LOW\|1E-11}}. For VASP versions prior to 6.4.0 this corresponds to {{TAGO\|ML_ISTART\|4}}.</p>		This mode automatically sets {{TAGO\|ML_LFAST\|.TRUE.}}, {{TAGO\|NSW\|1}}, {{TAGO\|ML_IALGO_LINREG\|4}}, {{TAGO\|ML_SIGW0\|1E-7}}, {{TAGO\|ML_SIGV0\|1}} and {{TAGO\|ML_EPS_LOW\|1E-11}}. For VASP versions prior to 6.4.0 this corresponds to {{TAGO\|ML_ISTART\|4}}.</p>
	</li><br />		</li><br />

Singraber at 22:21, 19 October 2023

2023-10-19T22:21:39Z

← Older revision		Revision as of 22:21, 19 October 2023
Line 32:		Line 32:
	The {{FILE\|ML_AB}} file may contain values for <tt>CTIFOR</tt> for each training structure. These are the thresholds used to sample that structure from the previous training. The thresholds found on the {{FILE\|ML_AB}} will be re-used unless a threshold is explicitly specified in the {{FILE\|INCAR}} file, by means of the {{TAG\|ML_CTIFOR}} tag. In the latter case the thresholds from the {{TAG\|ML_AB}} file are ignored. In case the {{FILE\|ML_AB}} contains '''no''' <tt>CTIFOR</tt> information and '''no''' threshold is specified in the {{FILE\|INCAR}} file, the default value for {{TAG\|ML_CTIFOR}} is used.</p><p>		The {{FILE\|ML_AB}} file may contain values for <tt>CTIFOR</tt> for each training structure. These are the thresholds used to sample that structure from the previous training. The thresholds found on the {{FILE\|ML_AB}} will be re-used unless a threshold is explicitly specified in the {{FILE\|INCAR}} file, by means of the {{TAG\|ML_CTIFOR}} tag. In the latter case the thresholds from the {{TAG\|ML_AB}} file are ignored. In case the {{FILE\|ML_AB}} contains '''no''' <tt>CTIFOR</tt> information and '''no''' threshold is specified in the {{FILE\|INCAR}} file, the default value for {{TAG\|ML_CTIFOR}} is used.</p><p>
	This mode automatically sets {{TAGO\|NSW\|1}} and {{TAGO\|ML_CDOUB\|4}}.For VASP versions prior to 6.4.0 this corresponds to {{TAGO\|ML_ISTART\|3}}.</p>		This mode automatically sets {{TAGO\|NSW\|1}} and {{TAGO\|ML_CDOUB\|4}}.For VASP versions prior to 6.4.0 this corresponds to {{TAGO\|ML_ISTART\|3}}.</p>
	</li>		</li><br />
	</ul>		<li>{{TAGO\|ML_MODE\|refit}}''': Refit a force field for "fast" evaluation'''<p>
			Similar to {{TAGO\|ML_MODE\|select}}, refitting is done based on an existing {{TAG\|ML_AB}} file, but the number of local reference configurations for each species is taken from the {{TAG\|ML_AB}} file. Sparsification is performed on the local reference configurations, so the resulting {{TAG\|ML_ABN}} file will contain the same number or fewer local reference configurations than the {{TAG\|ML_AB}} file.</p><p>
	*{{~~TAG~~\|ML_MODE}} ~~= refit~~ : ~~Construct~~ a force field ~~from an existing {{TAG\|ML_AB}} file, suited~~ for "fast" evaluation ~~({{TAG\|ML_LFAST}}<tt>=.TRUE.~~<~~/tt~~>).		By default the resulting force field is geared towards "fast" evaluation to speed up production runs ({{TAGO\|ML_LFAST\|.TRUE.}}). This comes at the cost of not being able to evaluate Bayesian error estimates.</p>
			{{NB\|warning\|We strongly advise to use {{TAGO\|ML_MODE\|refit}} if no error estimates are required during production runs.}}<p>
	~~:Sets: {{TAG\|ML_ISTART}} = 4, {{TAG\|ML_LFAST}} = .TRUE., {{TAG\|NSW}} = 1, {{TAG\|ML_IALGO_LINREG}} = 4, {{TAG\|ML_SIGW0}} = 1E-7, {{TAG\|ML_SIGV0}} = 1, and {{TAG\|ML_EPS_LOW}} = 1E-11.~~		This mode automatically sets {{TAGO\|ML_LFAST\|.TRUE.}}, {{TAGO\|NSW\|1}}, {{TAGO\|ML_IALGO_LINREG\|4}}, {{TAGO\|ML_SIGW0\|1E-7}}, {{TAGO\|ML_SIGV0\|1}} and {{TAGO\|ML_EPS_LOW\|1E-11}}. For VASP versions prior to 6.4.0 this corresponds to {{TAGO\|ML_ISTART\|4}}.</p>
			</li><br />
	:Similar to {{~~TAG~~\|ML_MODE}} ~~= SELECT~~, refitting is done based on an existing {{TAG\|ML_AB}} file, but the number of local reference configurations for each species is taken from the {{TAG\|ML_AB}} file. Sparsification is performed on the local reference configurations, so the resulting {{TAG\|ML_ABN}} file will contain the same number or fewer local reference configurations than the {{TAG\|ML_AB}} file.		<li>{{TAGO\|ML_MODE\|refitbayesian}}''': Refit a force field with Bayesian regression (deprecated)'''<p>
			Same as {{TAGO\|ML_MODE\|refit}}, but Bayesian regression is employed. This results in lower accuracy and much slower force fields than using {{TAGO\|ML_MODE\|refit}} and should be used with caution. On the other hand, this mode allows the generation of {{TAG\|ML_FFN}} files that can calculate Bayesian error estimates in addition to predictions.</p><p>
	:By default the resulting force field is geared towards "fast" evaluation to speed up production runs ({{~~TAG~~\|ML_LFAST~~}} =~~ .TRUE.). This comes at the cost of not being able to evaluate Bayesian error estimates.		This modes sets {{TAGO\|NSW\|1}}, {{TAGO\|ML_IALGO_LINREG\|1}} and {{TAGO\|ML_LFAST\|.FALSE.}}. For VASP versions prior to 6.4.0 this corresponds to {{TAGO\|ML_ISTART\|4}}.</p>
	{{NB\|warning\|We strongly advise to use {{~~TAG~~\|ML_MODE}} ~~{{=}} refit~~ if no error estimates are required during production runs.\|:}}		</li><br />
			<li>{{TAGO\|ML_MODE\|run}}''': Perform only force field predictions'''<p>
	*{{~~TAG~~\|~~ML_MODE~~}} ~~= refitbayesian (deprecated): Same as~~ {{~~TAG~~\|~~ML_MODE~~}} ~~= refit~~, ~~but Bayesian regression is employed.~~		A previously trained machine learning force field is read from the {{FILE\|ML_FF}} file, and the MD simulation is driven with predictions from the force field only. '''No''' ''ab initio'' calculations are performed and '''no''' learning is executed. This setting is typically used when the machine learning force field is considered mature and ready for production runs.</p><p>
			Optionally, if the force field was refitted using {{TAGO\|ML_MODE\|refitbayesian}}, the Bayesian error estimate of the energies, forces, and stress can be computed and logged in the {{FILE\|ML_LOGFILE}}. The output frequency of the Bayesian errors can be set via the {{TAG\|ML_IERR}} tag, the default is 0.</p><p>
	~~:Sets:~~ {{~~TAG~~\|~~ML_ISTART~~}} ~~= 4~~, {{~~TAG~~\|~~NSW~~}} ~~= 1~~, {{~~TAG~~\|~~ML_IALGO_LINREG~~}} ~~= 1,~~ and {{~~TAG~~\|~~ML_LFAST~~}}=.~~FALSE~~.		For VASP versions prior to 6.4.0 this corresponds to {{TAGO\|ML_ISTART\|2}}.</p>
			</li><br />
	:This results in lower accuracy and much slower force fields than using {{~~TAG~~\|ML_MODE}} ~~= refit~~ and should be used with caution. On the other hand, this mode allows the generation of {{TAG\|ML_FFN}} files that can calculate Bayesian error estimates.		<li>{{TAGO\|ML_MODE\|none}}''': The tag is ignored'''
			</ul><br />
	*{{~~TAG~~\|~~ML_MODE~~}} ~~= run : Force-field evaluation only~~.		{{NB\|warning\|If any option other than the above is chosen or there is a spelling error (be careful to write everything in upper case or lower case letters) the code will exit with an error.}}
			{{NB\|tip\|Some choices of {{TAG\|ML_MODE}} will automatically set other machine-learned force field tags. However, it is still possible to overwrite the defaults by specifying the corresponding tags in the {{FILE\|INCAR}} file.}}
	~~:Sets:~~ {{~~TAG~~\|ML_ISTART}}=2

	:A previously trained machine learning force field is read from the {{FILE\|ML_FF}} file, and the MD simulation is driven with predictions from the force field only, '''no''' ''ab initio'' calculations are performed and '''no''' learning is executed. This setting is typically used when the machine learning force field is considered mature and ready for production runs.
	:Optionally if the force field was refitted using {{~~TAG~~\|ML_MODE}} ~~= refitbayesian~~, the Bayesian error estimate of the energies, forces, and stress can be computed and logged in the {{FILE\|ML_LOGFILE}} ~~by setting the~~ output frequency of the Bayesian errors ~~by means of~~ the {{TAG\|ML_IERR}} tag~~. The~~ default is {{~~TAG~~\|~~ML_IERR~~}}=0.

	*{{~~TAG~~\|ML_MODE}} ~~= none (default)~~: The tag is ignored.
	{{NB\|warning\|If any option other than the above is chosen or ~~any of them~~ is ~~misspelled~~ (be careful to write everything in upper case or lower case letters) the code will exit with an error.}}
	{{NB\|tip\|~~The user may~~ overwrite the ~~default~~ by specifying ~~any of~~ the ~~machine learning~~ tags in the INCAR file.}}

	== Related tags and articles ==		== Related tags and articles ==

Singraber at 21:50, 19 October 2023

2023-10-19T21:50:03Z

← Older revision		Revision as of 21:50, 19 October 2023
Line 8:		Line 8:
	The following options are available for this tag:		The following options are available for this tag:

	*{{~~TAG~~\|ML_MODE}} ~~= train~~ : On-the-fly training.		<ul>
	:Force predictions from the machine learning force field are used to drive the molecular dynamics (MD) simulation. However, if the error estimation performed in each time step indicates a high force error an ab initio calculation is performed instead and the collected energy, forces, and stress are used to improve the machine learning force field.		<li>{{TAGO\|ML_MODE\|train}}''': On-the-fly training'''<p>
			Force predictions from the machine learning force field are used to drive the molecular dynamics (MD) simulation. However, if the error estimation performed in each time step indicates a high force error an ab initio calculation is performed instead and the collected energy, forces, and stress are used to improve the machine learning force field. There are two possible cases depending on, whether an {{TAG\|ML_AB}} is present in the calculation folder or not:</p>
			<ol>
			<li>
			No {{FILE\|ML_AB}} file found:<p>
			On-the-fly training is starting from scratch. Note that at the beginning of the MD run, when there is no force field available or it is still poorly trained, ''ab initio'' calculations will happen frequently. For VASP versions prior to 6.4.0 this corresponds to {{TAGO\|ML_ISTART\|0}}.</p>
			</li>
			<li>
			{{FILE\|ML_AB}} file present:<p>
			Restart on-the-fly training from existing training database. Before the MD run starts the {{FILE\|ML_AB}} file (a copy of the {{FILE\|ML_ABN}} from a previous training run!) is read and the ''ab initio'' data (energies, forces, and stresses) and local reference configurations it contains are used to generate an initial force field. Subsequently, the on-the-fly training MD is started. For VASP versions prior to 6.4.0 this corresponds to {{TAGO\|ML_ISTART\|1}}.</p>{{NB\|tip\|None of the structures in the {{FILE\|ML_AB}} file need to match he {{FILE\|POSCAR}} file for the current MD training run in terms of the simulation box, elements, or number of atoms. However, if the same elements appear the initial force field is used for predictions in the current MD run.

	~~:There are two possible cases depending on, whether an {{TAG\|ML_AB}} is present in the calculation folder or not:~~		The training data contained in the {{FILE\|ML_AB}} file is included in the final machine learning force field, i.e., the {{FILE\|ML_FFN}} file will define a force field applicable to both the structures on the {{FILE\|ML_AB}} file as well as to the current MD simulation. This means that by restarting repeatedly with {{TAGO\|ML_MODE\|train}}, and copying the {{FILE\|ML_ABN}} file from the previous run to {{FILE\|ML_AB}}(!), it is possible to iteratively extend the applicability of the a machine learning force field, e.g., by exploring different temperature ranges or element compositions.}}
			</li>
	**No {{TAG\|ML_AB}} file: On-the-fly training, starting from scratch.		</ol>
	~~::Sets: {{TAG\|ML_ISTART}} = 0~~		</li><br />
	~~::Note that at the beginning of the MD run, when there is no force field available or it is still poorly trained, ''ab initio'' calculations will happen frequently.~~		<li>
			{{TAGO\|ML_MODE\|select}}''': Re-selection of local reference configurations'''<p>
	**{{TAG\|ML_AB}} file present: Restart on-the-fly training.		A new machine learning force field is generated from the ''ab initio'' data provided in the {{FILE\|ML_AB}} file. The structures are read and processed one by one as if harvested in an MD simulation. In other words, the same steps are performed as in on-the-fly training but the source of the data are not actual ''ab initio'' calculations in an MD run but the series of structures available in the {{FILE\|ML_AB}} file. The list of local reference configurations on the {{FILE\|ML_AB}} file will be ignored. Instead a new collection of local reference configurations is determined and written to the resulting {{FILE\|ML_ABN}} file.</p>
	~~::Sets: {{TAG\|ML_ISTART}} = 1~~		{{NB\|important\|This operation mode allows to generate a {{VASP}} machine learning force field from pre-computed or external ''ab initio'' data sets. In contrast to {{TAGO\|ML_MODE\|refit}} also the local reference configurations are selected from the entire data set. For example, an {{FILE\|ML_AB}} file created manually by extracting ''ab initio'' data (energies, forces, stresses) from {{FILE\|OUTCAR}} files (or even other external sources) can be processed in this mode without prior knowledge of local reference configurations (only a dummy section must be added to the {{FILE\|ML_AB}} file, see its documentation). Similar to on-the-fly training this mode generates {{FILE\|ML_FFN}} files and {{FILE\|ML_ABN}} '''with''' local reference configurations.}}<p>
	::Before the MD run starts the {{FILE\|ML_AB}} file (a copy of the {{FILE\|ML_ABN}} from a previous training run!) is read and the ''ab initio'' data (energies, forces, and stresses) and local reference configurations it contains are used to generate an initial force field. Subsequently, the on-the-fly training MD is started.		A new iteration through the training structures can lead to a frequent update of the force field. This is quite time-consuming. Hence, for this mode the default value of {{TAG\|ML_CDOUB}} is automatically increased from 2 to 4 which will result in a much less frequent update of the force field. This leads to much more efficient calculations while practically not changing the results.</p>
			{{NB\|tip\|If calculations for {{TAGO\|ML_MODE\|select}} are too time-consuming, it is useful to increase {{TAG\|ML_MCONF_NEW}} to values around 10-16. Together with {{TAGO\|ML_CDOUB\|4}}, this often accelerates the calculations by a factor of 2-4.}}<p>
	::'''N.B.I''': None of the structures in the {{FILE\|ML_AB}} file need to match he {{FILE\|POSCAR}} file for the current MD training run in terms of the simulation box, elements, or number of atoms. However, if the same elements appear the initial force field is used for predictions in the current MD run.		The {{FILE\|ML_AB}} file may contain values for <tt>CTIFOR</tt> for each training structure. These are the thresholds used to sample that structure from the previous training. The thresholds found on the {{FILE\|ML_AB}} will be re-used unless a threshold is explicitly specified in the {{FILE\|INCAR}} file, by means of the {{TAG\|ML_CTIFOR}} tag. In the latter case the thresholds from the {{TAG\|ML_AB}} file are ignored. In case the {{FILE\|ML_AB}} contains '''no''' <tt>CTIFOR</tt> information and '''no''' threshold is specified in the {{FILE\|INCAR}} file, the default value for {{TAG\|ML_CTIFOR}} is used.</p><p>
	~~::'''N.B.II''':~~ The training data contained in the {{FILE\|ML_AB}} file is included in the final machine learning force field, ''i.e.'' the {{FILE\|ML_FFN}} file will define a force field applicable to both the structures on the {{FILE\|ML_AB}} file as well as to the current MD simulation. This means that by restarting repeatedly with {{~~TAG~~\|ML_MODE}}~~=train~~, and copying the {{FILE\|ML_ABN}} file from the previous run to {{FILE\|ML_AB}}(!), it is possible to iteratively extend the applicability of the a machine learning force field, ''e.g.'' by exploring different temperature ranges or element compositions.		This mode automatically sets {{TAGO\|NSW\|1}} and {{TAGO\|ML_CDOUB\|4}}.For VASP versions prior to 6.4.0 this corresponds to {{TAGO\|ML_ISTART\|3}}.</p>
			</li>
	*{{~~TAG~~\|ML_MODE}} ~~= select~~ : ~~Train a force field by re~~-selection of local reference configurations ~~from an existing {{TAG\|ML_AB}} file.~~		</ul>

	~~:Sets: {{TAG\|ML_ISTART}} = 3, {{TAG\|NSW}} = 1, and {{TAG\|ML_CDOUB}} = 4~~

	:A new machine learning force field is generated from the ''ab initio'' data provided in the {{FILE\|ML_AB}} file. The structures are read and processed one by one as if harvested in an MD simulation. In other words, the same steps are performed as in on-the-fly training but the source of the data are not actual ''ab initio'' calculations in an MD run but the series of structures available in the {{FILE\|ML_AB}} file.

	**The list of local reference configurations on the {{FILE\|ML_AB}} file will be ignored. Instead a new collection of local reference configurations is determined and written to the resulting {{FILE\|ML_ABN}} file.

	**A new iteration through the training structures can lead to a frequent update of the force field. This is quite time-consuming. ~~Increasing~~ {{TAG\|ML_CDOUB}} from 2 to 4 ~~for this mode~~ will result in a much less frequent update of the force field. This leads to much more efficient calculations while practically not changing the results.

	**The {{FILE\|ML_AB}} file may contain values for <tt>CTIFOR</tt> for each training structure. These are the thresholds used to sample that structure from the previous training. The thresholds found on the {{FILE\|ML_AB}} will be re-used unless a threshold is explicitly specified in the {{~~TAG~~\|INCAR}} file, by means of the {{TAG\|ML_CTIFOR}} tag. In the latter case the thresholds from the {{TAG\|ML_AB}} file are ignored. In case the {{FILE\|ML_AB}} contains '''no''' <tt>CTIFOR</tt> information and '''no''' threshold is specified in the {{FILE\|INCAR}} file, the default value for {{TAG\|ML_CTIFOR}} is used.
	{{NB\|~~tip~~\|~~If calculations for {{TAG\|ML_MODE~~}} {{~~=}} SELECT are too time-consuming, it is useful to increase {{TAG~~\|~~ML_MCONF_NEW~~}} to ~~values around 10-16~~. ~~Together with {{TAG\|ML_CDOUB}} {{=}}~~ 4, this ~~often accelerates the calculations by a factor of 2-4.\|:}}~~
	{{NB\|~~mind~~\|~~This operation mode needs to be used to generate {{VASP~~}} ~~machine learning force fields from pre-computed or external ab initio data sets~~.~~\|:}}~~

	*{{TAG\|ML_MODE}} = refit : Construct a force field from an existing {{TAG\|ML_AB}} file, suited for "fast" evaluation ({{TAG\|ML_LFAST}}<tt>=.TRUE.</tt>).		*{{TAG\|ML_MODE}} = refit : Construct a force field from an existing {{TAG\|ML_AB}} file, suited for "fast" evaluation ({{TAG\|ML_LFAST}}<tt>=.TRUE.</tt>).
Line 61:		Line 58:

	*{{TAG\|ML_MODE}} = none (default): The tag is ignored.		*{{TAG\|ML_MODE}} = none (default): The tag is ignored.

	{{NB\|warning\|If any option other than the above is chosen or any of them is misspelled (be careful to write everything in upper case or lower case letters) the code will exit with an error.}}		{{NB\|warning\|If any option other than the above is chosen or any of them is misspelled (be careful to write everything in upper case or lower case letters) the code will exit with an error.}}

	{{NB\|tip\|The user may overwrite the default by specifying any of the machine learning tags in the INCAR file.}}		{{NB\|tip\|The user may overwrite the default by specifying any of the machine learning tags in the INCAR file.}}

Vaspmaster at 12:44, 17 April 2023

2023-04-17T12:44:12Z

← Older revision		Revision as of 12:44, 17 April 2023
Line 35:		Line 35:

	**The {{FILE\|ML_AB}} file may contain values for <tt>CTIFOR</tt> for each training structure. These are the thresholds used to sample that structure from the previous training. The thresholds found on the {{FILE\|ML_AB}} will be re-used unless a threshold is explicitly specified in the {{TAG\|INCAR}} file, by means of the {{TAG\|ML_CTIFOR}} tag. In the latter case the thresholds from the {{TAG\|ML_AB}} file are ignored. In case the {{FILE\|ML_AB}} contains '''no''' <tt>CTIFOR</tt> information and '''no''' threshold is specified in the {{FILE\|INCAR}} file, the default value for {{TAG\|ML_CTIFOR}} is used.		**The {{FILE\|ML_AB}} file may contain values for <tt>CTIFOR</tt> for each training structure. These are the thresholds used to sample that structure from the previous training. The thresholds found on the {{FILE\|ML_AB}} will be re-used unless a threshold is explicitly specified in the {{TAG\|INCAR}} file, by means of the {{TAG\|ML_CTIFOR}} tag. In the latter case the thresholds from the {{TAG\|ML_AB}} file are ignored. In case the {{FILE\|ML_AB}} contains '''no''' <tt>CTIFOR</tt> information and '''no''' threshold is specified in the {{FILE\|INCAR}} file, the default value for {{TAG\|ML_CTIFOR}} is used.
	{{NB\|tip\|If calculations for {{TAG\|ML_MODE}} {{=}} SELECT are too time-consuming, it is useful to increase {{TAG\|ML_MCONF_NEW}} to values around 10-16. Together with {{TAG\|ML_CDOUB}} {{=}} 4, this often accelerates the calculations by a factor of 2-4.}}		{{NB\|tip\|If calculations for {{TAG\|ML_MODE}} {{=}} SELECT are too time-consuming, it is useful to increase {{TAG\|ML_MCONF_NEW}} to values around 10-16. Together with {{TAG\|ML_CDOUB}} {{=}} 4, this often accelerates the calculations by a factor of 2-4.\|:}}
	{{NB\|mind\|This operation mode needs to be used to generate {{VASP}} machine learning force fields from pre-computed or external ab initio data sets.}}		{{NB\|mind\|This operation mode needs to be used to generate {{VASP}} machine learning force fields from pre-computed or external ab initio data sets.\|:}}

	*{{TAG\|ML_MODE}} = refit : Construct a force field from an existing {{TAG\|ML_AB}} file, suited for "fast" evaluation ({{TAG\|ML_LFAST}}<tt>=.TRUE.</tt>).		*{{TAG\|ML_MODE}} = refit : Construct a force field from an existing {{TAG\|ML_AB}} file, suited for "fast" evaluation ({{TAG\|ML_LFAST}}<tt>=.TRUE.</tt>).
Line 45:		Line 45:

	:By default the resulting force field is geared towards "fast" evaluation to speed up production runs ({{TAG\|ML_LFAST}} = .TRUE.). This comes at the cost of not being able to evaluate Bayesian error estimates.		:By default the resulting force field is geared towards "fast" evaluation to speed up production runs ({{TAG\|ML_LFAST}} = .TRUE.). This comes at the cost of not being able to evaluate Bayesian error estimates.
	{{NB\|warning\|We strongly advise to use {{TAG\|ML_MODE}} {{=}} refit if no error estimates are required during production runs.}}		{{NB\|warning\|We strongly advise to use {{TAG\|ML_MODE}} {{=}} refit if no error estimates are required during production runs.\|:}}

	*{{TAG\|ML_MODE}} = refitbayesian (deprecated): Same as {{TAG\|ML_MODE}} = refit, but Bayesian regression is employed.		*{{TAG\|ML_MODE}} = refitbayesian (deprecated): Same as {{TAG\|ML_MODE}} = refit, but Bayesian regression is employed.

Vaspmaster at 17:49, 14 April 2023

2023-04-14T17:49:00Z

← Older revision		Revision as of 17:49, 14 April 2023
Line 60:		Line 60:
	:Optionally if the force field was refitted using {{TAG\|ML_MODE}} = refitbayesian, the Bayesian error estimate of the energies, forces, and stress can be computed and logged in the {{FILE\|ML_LOGFILE}} by setting the output frequency of the Bayesian errors by means of the {{TAG\|ML_IERR}} tag. The default is {{TAG\|ML_IERR}}=0.		:Optionally if the force field was refitted using {{TAG\|ML_MODE}} = refitbayesian, the Bayesian error estimate of the energies, forces, and stress can be computed and logged in the {{FILE\|ML_LOGFILE}} by setting the output frequency of the Bayesian errors by means of the {{TAG\|ML_IERR}} tag. The default is {{TAG\|ML_IERR}}=0.

	*{{TAG\|ML_MODE}} = none : ~~This~~ tag is ~~not used~~.		*{{TAG\|ML_MODE}} = none (default): The tag is ignored.

	{{NB\|warning\|If any option other than the above is chosen or any of them is misspelled (be careful to write everything in upper case or lower case letters) the code will exit with an error.}}		{{NB\|warning\|If any option other than the above is chosen or any of them is misspelled (be careful to write everything in upper case or lower case letters) the code will exit with an error.}}

← Older revision		Revision as of 14:43, 20 August 2024
Line 32:		Line 32:
	The {{FILE\|ML_AB}} file may contain values for <tt>CTIFOR</tt> for each training structure. These are the thresholds used to sample that structure from the previous training. The thresholds found on the {{FILE\|ML_AB}} will be re-used unless a threshold is explicitly specified in the {{FILE\|INCAR}} file, by means of the {{TAG\|ML_CTIFOR}} tag. In the latter case the thresholds from the {{TAG\|ML_AB}} file are ignored. In case the {{FILE\|ML_AB}} contains '''no''' <tt>CTIFOR</tt> information and '''no''' threshold is specified in the {{FILE\|INCAR}} file, the default value for {{TAG\|ML_CTIFOR}} is used.</p><p>		The {{FILE\|ML_AB}} file may contain values for <tt>CTIFOR</tt> for each training structure. These are the thresholds used to sample that structure from the previous training. The thresholds found on the {{FILE\|ML_AB}} will be re-used unless a threshold is explicitly specified in the {{FILE\|INCAR}} file, by means of the {{TAG\|ML_CTIFOR}} tag. In the latter case the thresholds from the {{TAG\|ML_AB}} file are ignored. In case the {{FILE\|ML_AB}} contains '''no''' <tt>CTIFOR</tt> information and '''no''' threshold is specified in the {{FILE\|INCAR}} file, the default value for {{TAG\|ML_CTIFOR}} is used.</p><p>
	This mode automatically sets {{TAGO\|NSW\|1}} and {{TAGO\|ML_CDOUB\|4}}.For VASP versions prior to 6.4.0 this corresponds to {{TAGO\|ML_ISTART\|3}}.		This mode automatically sets {{TAGO\|NSW\|1}} and {{TAGO\|ML_CDOUB\|4}}.For VASP versions prior to 6.4.0 this corresponds to {{TAGO\|ML_ISTART\|3}}.
	{{NB\|warning\|{{TAGO\|ML_MODE\|select}} ignores the {{TAG\|POSCAR}} and hence no ~~prediction is~~ made at the end of this calculation (instead zeros are written to stdout, {{TAG\|OSZICAR}} and {{TAG\|OUTCAR}}).}}</p>		{{NB\|warning\|{{TAGO\|ML_MODE\|select}} ignores the structure in the {{TAG\|POSCAR}} and hence no error, force and stress predictions are made at the end of this calculation (instead zeros are written to stdout, {{TAG\|OSZICAR}} and {{TAG\|OUTCAR}}).}}</p>
	</li><br />		</li><br />
	<li>{{TAGO\|ML_MODE\|refit}}''': Refit a force field for "fast" evaluation'''<p>		<li>{{TAGO\|ML_MODE\|refit}}''': Refit a force field for "fast" evaluation'''<p>

← Older revision		Revision as of 14:41, 20 August 2024
Line 32:		Line 32:
	The {{FILE\|ML_AB}} file may contain values for <tt>CTIFOR</tt> for each training structure. These are the thresholds used to sample that structure from the previous training. The thresholds found on the {{FILE\|ML_AB}} will be re-used unless a threshold is explicitly specified in the {{FILE\|INCAR}} file, by means of the {{TAG\|ML_CTIFOR}} tag. In the latter case the thresholds from the {{TAG\|ML_AB}} file are ignored. In case the {{FILE\|ML_AB}} contains '''no''' <tt>CTIFOR</tt> information and '''no''' threshold is specified in the {{FILE\|INCAR}} file, the default value for {{TAG\|ML_CTIFOR}} is used.</p><p>		The {{FILE\|ML_AB}} file may contain values for <tt>CTIFOR</tt> for each training structure. These are the thresholds used to sample that structure from the previous training. The thresholds found on the {{FILE\|ML_AB}} will be re-used unless a threshold is explicitly specified in the {{FILE\|INCAR}} file, by means of the {{TAG\|ML_CTIFOR}} tag. In the latter case the thresholds from the {{TAG\|ML_AB}} file are ignored. In case the {{FILE\|ML_AB}} contains '''no''' <tt>CTIFOR</tt> information and '''no''' threshold is specified in the {{FILE\|INCAR}} file, the default value for {{TAG\|ML_CTIFOR}} is used.</p><p>
	This mode automatically sets {{TAGO\|NSW\|1}} and {{TAGO\|ML_CDOUB\|4}}.For VASP versions prior to 6.4.0 this corresponds to {{TAGO\|ML_ISTART\|3}}.		This mode automatically sets {{TAGO\|NSW\|1}} and {{TAGO\|ML_CDOUB\|4}}.For VASP versions prior to 6.4.0 this corresponds to {{TAGO\|ML_ISTART\|3}}.
	{{NB\|warning\|{{TAGO\|ML_MODE\|select}} ignores the {{TAG\|POSCAR}} and hence no prediction is made at the end of this calculation (instead zeros are written to stdout, {{TAG\|OSZICAR}} and {{TAG\|OUTCAR}}.}}</p>		{{NB\|warning\|{{TAGO\|ML_MODE\|select}} ignores the {{TAG\|POSCAR}} and hence no prediction is made at the end of this calculation (instead zeros are written to stdout, {{TAG\|OSZICAR}} and {{TAG\|OUTCAR}}).}}</p>
	</li><br />		</li><br />
	<li>{{TAGO\|ML_MODE\|refit}}''': Refit a force field for "fast" evaluation'''<p>		<li>{{TAGO\|ML_MODE\|refit}}''': Refit a force field for "fast" evaluation'''<p>

← Older revision		Revision as of 14:41, 20 August 2024
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	{{NB\|tip\|If calculations for {{TAGO\|ML_MODE\|select}} are too time-consuming, it is useful to increase {{TAG\|ML_MCONF_NEW}} to values around 10-16. Together with {{TAGO\|ML_CDOUB\|4}}, this often accelerates the calculations by a factor of 2-4.}}<p>		{{NB\|tip\|If calculations for {{TAGO\|ML_MODE\|select}} are too time-consuming, it is useful to increase {{TAG\|ML_MCONF_NEW}} to values around 10-16. Together with {{TAGO\|ML_CDOUB\|4}}, this often accelerates the calculations by a factor of 2-4.}}<p>
	The {{FILE\|ML_AB}} file may contain values for <tt>CTIFOR</tt> for each training structure. These are the thresholds used to sample that structure from the previous training. The thresholds found on the {{FILE\|ML_AB}} will be re-used unless a threshold is explicitly specified in the {{FILE\|INCAR}} file, by means of the {{TAG\|ML_CTIFOR}} tag. In the latter case the thresholds from the {{TAG\|ML_AB}} file are ignored. In case the {{FILE\|ML_AB}} contains '''no''' <tt>CTIFOR</tt> information and '''no''' threshold is specified in the {{FILE\|INCAR}} file, the default value for {{TAG\|ML_CTIFOR}} is used.</p><p>		The {{FILE\|ML_AB}} file may contain values for <tt>CTIFOR</tt> for each training structure. These are the thresholds used to sample that structure from the previous training. The thresholds found on the {{FILE\|ML_AB}} will be re-used unless a threshold is explicitly specified in the {{FILE\|INCAR}} file, by means of the {{TAG\|ML_CTIFOR}} tag. In the latter case the thresholds from the {{TAG\|ML_AB}} file are ignored. In case the {{FILE\|ML_AB}} contains '''no''' <tt>CTIFOR</tt> information and '''no''' threshold is specified in the {{FILE\|INCAR}} file, the default value for {{TAG\|ML_CTIFOR}} is used.</p><p>
	This mode automatically sets {{TAGO\|NSW\|1}} and {{TAGO\|ML_CDOUB\|4}}.For VASP versions prior to 6.4.0 this corresponds to {{TAGO\|ML_ISTART\|3}}.</p>		This mode automatically sets {{TAGO\|NSW\|1}} and {{TAGO\|ML_CDOUB\|4}}.For VASP versions prior to 6.4.0 this corresponds to {{TAGO\|ML_ISTART\|3}}.
			{{NB\|warning\|{{TAGO\|ML_MODE\|select}} ignores the {{TAG\|POSCAR}} and hence no prediction is made at the end of this calculation (instead zeros are written to stdout, {{TAG\|OSZICAR}} and {{TAG\|OUTCAR}}.}}</p>
	</li><br />		</li><br />
	<li>{{TAGO\|ML_MODE\|refit}}''': Refit a force field for "fast" evaluation'''<p>		<li>{{TAGO\|ML_MODE\|refit}}''': Refit a force field for "fast" evaluation'''<p>
	Similar to {{TAGO\|ML_MODE\|select}}, refitting is done based on an existing {{TAG\|ML_AB}} file, but the number of local reference configurations for each species is taken from the {{TAG\|ML_AB}} file. Sparsification is performed on the local reference configurations, so the resulting {{TAG\|ML_ABN}} file will contain the same number or fewer local reference configurations than the {{TAG\|ML_AB}} file.</p><p>		Similar to {{TAGO\|ML_MODE\|select}}, refitting is done based on an existing {{TAG\|ML_AB}} file, but the number of local reference configurations for each species is taken from the {{TAG\|ML_AB}} file. Sparsification is performed on the local reference configurations, so the resulting {{TAG\|ML_ABN}} file will contain the same number or fewer local reference configurations than the {{TAG\|ML_AB}} file.</p><p>
	By default the resulting force field is geared towards "fast" evaluation to speed up production runs ({{TAGO\|ML_LFAST\|.TRUE.}}). This comes at the cost of not being able to evaluate Bayesian error estimates.</p>		By default the resulting force field is geared towards "fast" evaluation to speed up production runs ({{TAGO\|ML_LFAST\|.TRUE.}}). This comes at the cost of not being able to evaluate Bayesian error estimates.</p>
	{{NB\|warning\|We strongly advise to use {{TAGO\|ML_MODE\|refit}} if no error estimates are required during production runs.}}<p>		{{NB\|warning\|We strongly advise to use {{TAGO\|ML_MODE\|refit}} if no Bayesian error estimates are required during production runs.}}<p>
	This mode automatically sets {{TAGO\|ML_LFAST\|.TRUE.}}, {{TAGO\|NSW\|1}}, {{TAGO\|ML_IALGO_LINREG\|4}}, {{TAGO\|ML_SIGW0\|1E-7}}, {{TAGO\|ML_SIGV0\|1}} and {{TAGO\|ML_EPS_LOW\|1E-11}}. For VASP versions prior to 6.4.0 this corresponds to {{TAGO\|ML_ISTART\|4}}.</p>		This mode automatically sets {{TAGO\|ML_LFAST\|.TRUE.}}, {{TAGO\|NSW\|1}}, {{TAGO\|ML_IALGO_LINREG\|4}}, {{TAGO\|ML_SIGW0\|1E-7}}, {{TAGO\|ML_SIGV0\|1}} and {{TAGO\|ML_EPS_LOW\|1E-11}}. For VASP versions prior to 6.4.0 this corresponds to {{TAGO\|ML_ISTART\|4}}.</p>
	</li><br />		</li><br />

← Older revision		Revision as of 22:21, 19 October 2023
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	The {{FILE\|ML_AB}} file may contain values for <tt>CTIFOR</tt> for each training structure. These are the thresholds used to sample that structure from the previous training. The thresholds found on the {{FILE\|ML_AB}} will be re-used unless a threshold is explicitly specified in the {{FILE\|INCAR}} file, by means of the {{TAG\|ML_CTIFOR}} tag. In the latter case the thresholds from the {{TAG\|ML_AB}} file are ignored. In case the {{FILE\|ML_AB}} contains '''no''' <tt>CTIFOR</tt> information and '''no''' threshold is specified in the {{FILE\|INCAR}} file, the default value for {{TAG\|ML_CTIFOR}} is used.</p><p>		The {{FILE\|ML_AB}} file may contain values for <tt>CTIFOR</tt> for each training structure. These are the thresholds used to sample that structure from the previous training. The thresholds found on the {{FILE\|ML_AB}} will be re-used unless a threshold is explicitly specified in the {{FILE\|INCAR}} file, by means of the {{TAG\|ML_CTIFOR}} tag. In the latter case the thresholds from the {{TAG\|ML_AB}} file are ignored. In case the {{FILE\|ML_AB}} contains '''no''' <tt>CTIFOR</tt> information and '''no''' threshold is specified in the {{FILE\|INCAR}} file, the default value for {{TAG\|ML_CTIFOR}} is used.</p><p>
	This mode automatically sets {{TAGO\|NSW\|1}} and {{TAGO\|ML_CDOUB\|4}}.For VASP versions prior to 6.4.0 this corresponds to {{TAGO\|ML_ISTART\|3}}.</p>		This mode automatically sets {{TAGO\|NSW\|1}} and {{TAGO\|ML_CDOUB\|4}}.For VASP versions prior to 6.4.0 this corresponds to {{TAGO\|ML_ISTART\|3}}.</p>
	</li>		</li><br />
	</ul>		<li>{{TAGO\|ML_MODE\|refit}}''': Refit a force field for "fast" evaluation'''<p>
			Similar to {{TAGO\|ML_MODE\|select}}, refitting is done based on an existing {{TAG\|ML_AB}} file, but the number of local reference configurations for each species is taken from the {{TAG\|ML_AB}} file. Sparsification is performed on the local reference configurations, so the resulting {{TAG\|ML_ABN}} file will contain the same number or fewer local reference configurations than the {{TAG\|ML_AB}} file.</p><p>
	*{{~~TAG~~\|ML_MODE}} ~~= refit~~ : ~~Construct~~ a force field ~~from an existing {{TAG\|ML_AB}} file, suited~~ for "fast" evaluation ~~({{TAG\|ML_LFAST}}<tt>=.TRUE.~~<~~/tt~~>).		By default the resulting force field is geared towards "fast" evaluation to speed up production runs ({{TAGO\|ML_LFAST\|.TRUE.}}). This comes at the cost of not being able to evaluate Bayesian error estimates.</p>
			{{NB\|warning\|We strongly advise to use {{TAGO\|ML_MODE\|refit}} if no error estimates are required during production runs.}}<p>
	~~:Sets: {{TAG\|ML_ISTART}} = 4, {{TAG\|ML_LFAST}} = .TRUE., {{TAG\|NSW}} = 1, {{TAG\|ML_IALGO_LINREG}} = 4, {{TAG\|ML_SIGW0}} = 1E-7, {{TAG\|ML_SIGV0}} = 1, and {{TAG\|ML_EPS_LOW}} = 1E-11.~~		This mode automatically sets {{TAGO\|ML_LFAST\|.TRUE.}}, {{TAGO\|NSW\|1}}, {{TAGO\|ML_IALGO_LINREG\|4}}, {{TAGO\|ML_SIGW0\|1E-7}}, {{TAGO\|ML_SIGV0\|1}} and {{TAGO\|ML_EPS_LOW\|1E-11}}. For VASP versions prior to 6.4.0 this corresponds to {{TAGO\|ML_ISTART\|4}}.</p>
			</li><br />
	:Similar to {{~~TAG~~\|ML_MODE}} ~~= SELECT~~, refitting is done based on an existing {{TAG\|ML_AB}} file, but the number of local reference configurations for each species is taken from the {{TAG\|ML_AB}} file. Sparsification is performed on the local reference configurations, so the resulting {{TAG\|ML_ABN}} file will contain the same number or fewer local reference configurations than the {{TAG\|ML_AB}} file.		<li>{{TAGO\|ML_MODE\|refitbayesian}}''': Refit a force field with Bayesian regression (deprecated)'''<p>
			Same as {{TAGO\|ML_MODE\|refit}}, but Bayesian regression is employed. This results in lower accuracy and much slower force fields than using {{TAGO\|ML_MODE\|refit}} and should be used with caution. On the other hand, this mode allows the generation of {{TAG\|ML_FFN}} files that can calculate Bayesian error estimates in addition to predictions.</p><p>
	:By default the resulting force field is geared towards "fast" evaluation to speed up production runs ({{~~TAG~~\|ML_LFAST~~}} =~~ .TRUE.). This comes at the cost of not being able to evaluate Bayesian error estimates.		This modes sets {{TAGO\|NSW\|1}}, {{TAGO\|ML_IALGO_LINREG\|1}} and {{TAGO\|ML_LFAST\|.FALSE.}}. For VASP versions prior to 6.4.0 this corresponds to {{TAGO\|ML_ISTART\|4}}.</p>
	{{NB\|warning\|We strongly advise to use {{~~TAG~~\|ML_MODE}} ~~{{=}} refit~~ if no error estimates are required during production runs.\|:}}		</li><br />
			<li>{{TAGO\|ML_MODE\|run}}''': Perform only force field predictions'''<p>
	*{{~~TAG~~\|~~ML_MODE~~}} ~~= refitbayesian (deprecated): Same as~~ {{~~TAG~~\|~~ML_MODE~~}} ~~= refit~~, ~~but Bayesian regression is employed.~~		A previously trained machine learning force field is read from the {{FILE\|ML_FF}} file, and the MD simulation is driven with predictions from the force field only. '''No''' ''ab initio'' calculations are performed and '''no''' learning is executed. This setting is typically used when the machine learning force field is considered mature and ready for production runs.</p><p>
			Optionally, if the force field was refitted using {{TAGO\|ML_MODE\|refitbayesian}}, the Bayesian error estimate of the energies, forces, and stress can be computed and logged in the {{FILE\|ML_LOGFILE}}. The output frequency of the Bayesian errors can be set via the {{TAG\|ML_IERR}} tag, the default is 0.</p><p>
	~~:Sets:~~ {{~~TAG~~\|~~ML_ISTART~~}} ~~= 4~~, {{~~TAG~~\|~~NSW~~}} ~~= 1~~, {{~~TAG~~\|~~ML_IALGO_LINREG~~}} ~~= 1,~~ and {{~~TAG~~\|~~ML_LFAST~~}}=.~~FALSE~~.		For VASP versions prior to 6.4.0 this corresponds to {{TAGO\|ML_ISTART\|2}}.</p>
			</li><br />
	:This results in lower accuracy and much slower force fields than using {{~~TAG~~\|ML_MODE}} ~~= refit~~ and should be used with caution. On the other hand, this mode allows the generation of {{TAG\|ML_FFN}} files that can calculate Bayesian error estimates.		<li>{{TAGO\|ML_MODE\|none}}''': The tag is ignored'''
			</ul><br />
	*{{~~TAG~~\|~~ML_MODE~~}} ~~= run : Force-field evaluation only~~.		{{NB\|warning\|If any option other than the above is chosen or there is a spelling error (be careful to write everything in upper case or lower case letters) the code will exit with an error.}}
			{{NB\|tip\|Some choices of {{TAG\|ML_MODE}} will automatically set other machine-learned force field tags. However, it is still possible to overwrite the defaults by specifying the corresponding tags in the {{FILE\|INCAR}} file.}}
	~~:Sets:~~ {{~~TAG~~\|ML_ISTART}}=2

	:A previously trained machine learning force field is read from the {{FILE\|ML_FF}} file, and the MD simulation is driven with predictions from the force field only, '''no''' ''ab initio'' calculations are performed and '''no''' learning is executed. This setting is typically used when the machine learning force field is considered mature and ready for production runs.
	:Optionally if the force field was refitted using {{~~TAG~~\|ML_MODE}} ~~= refitbayesian~~, the Bayesian error estimate of the energies, forces, and stress can be computed and logged in the {{FILE\|ML_LOGFILE}} ~~by setting the~~ output frequency of the Bayesian errors ~~by means of~~ the {{TAG\|ML_IERR}} tag~~. The~~ default is {{~~TAG~~\|~~ML_IERR~~}}=0.

	*{{~~TAG~~\|ML_MODE}} ~~= none (default)~~: The tag is ignored.
	{{NB\|warning\|If any option other than the above is chosen or ~~any of them~~ is ~~misspelled~~ (be careful to write everything in upper case or lower case letters) the code will exit with an error.}}
	{{NB\|tip\|~~The user may~~ overwrite the ~~default~~ by specifying ~~any of~~ the ~~machine learning~~ tags in the INCAR file.}}

	== Related tags and articles ==		== Related tags and articles ==

← Older revision		Revision as of 08:02, 24 October 2025
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	<ul>		<ul>
	<li>{{~~TAGO~~\|ML_MODE\|train}}''': On-the-fly training'''<p>		<li>{{TAG\|ML_MODE\|train}}''': On-the-fly training'''<p>
	Force predictions from the machine learning force field are used to drive the molecular dynamics (MD) simulation. However, if the error estimation performed in each time step indicates a high force error an ab initio calculation is performed instead and the collected energy, forces, and stress are used to improve the machine learning force field. There are two possible cases depending on, whether an {{TAG\|ML_AB}} is present in the calculation folder or not:</p>		Force predictions from the machine learning force field are used to drive the molecular dynamics (MD) simulation. However, if the error estimation performed in each time step indicates a high force error an ab initio calculation is performed instead and the collected energy, forces, and stress are used to improve the machine learning force field. There are two possible cases depending on, whether an {{TAG\|ML_AB}} is present in the calculation folder or not:</p>
	<ol>		<ol>
	<li>		<li>
	No {{FILE\|ML_AB}} file found:<p>		No {{FILE\|ML_AB}} file found:<p>
	On-the-fly training is starting from scratch. Note that at the beginning of the MD run, when there is no force field available or it is still poorly trained, ''ab initio'' calculations will happen frequently. For VASP versions prior to 6.4.0 this corresponds to {{~~TAGO~~\|ML_ISTART\|0}}.</p>		On-the-fly training is starting from scratch. Note that at the beginning of the MD run, when there is no force field available or it is still poorly trained, ''ab initio'' calculations will happen frequently. For VASP versions prior to 6.4.0 this corresponds to {{TAG\|ML_ISTART\|0}}.</p>
	</li>		</li>
	<li>		<li>
	{{FILE\|ML_AB}} file present:<p>		{{FILE\|ML_AB}} file present:<p>
	Restart on-the-fly training from existing training database. Before the MD run starts the {{FILE\|ML_AB}} file (a copy of the {{FILE\|ML_ABN}} from a previous training run!) is read and the ''ab initio'' data (energies, forces, and stresses) and local reference configurations it contains are used to generate an initial force field. Subsequently, the on-the-fly training MD is started. For VASP versions prior to 6.4.0 this corresponds to {{~~TAGO~~\|ML_ISTART\|1}}.</p>{{NB\|tip\|None of the structures in the {{FILE\|ML_AB}} file need to match he {{FILE\|POSCAR}} file for the current MD training run in terms of the simulation box, elements, or number of atoms. However, if the same elements appear the initial force field is used for predictions in the current MD run.		Restart on-the-fly training from existing training database. Before the MD run starts the {{FILE\|ML_AB}} file (a copy of the {{FILE\|ML_ABN}} from a previous training run!) is read and the ''ab initio'' data (energies, forces, and stresses) and local reference configurations it contains are used to generate an initial force field. Subsequently, the on-the-fly training MD is started. For VASP versions prior to 6.4.0 this corresponds to {{TAG\|ML_ISTART\|1}}.</p>{{NB\|tip\|None of the structures in the {{FILE\|ML_AB}} file need to match he {{FILE\|POSCAR}} file for the current MD training run in terms of the simulation box, elements, or number of atoms. However, if the same elements appear the initial force field is used for predictions in the current MD run.

	The training data contained in the {{FILE\|ML_AB}} file is included in the final machine learning force field, i.e., the {{FILE\|ML_FFN}} file will define a force field applicable to both the structures on the {{FILE\|ML_AB}} file as well as to the current MD simulation. This means that by restarting repeatedly with {{~~TAGO~~\|ML_MODE\|train}}, and copying the {{FILE\|ML_ABN}} file from the previous run to {{FILE\|ML_AB}}(!), it is possible to iteratively extend the applicability of the a machine learning force field, e.g., by exploring different temperature ranges or element compositions.}}		The training data contained in the {{FILE\|ML_AB}} file is included in the final machine learning force field, i.e., the {{FILE\|ML_FFN}} file will define a force field applicable to both the structures on the {{FILE\|ML_AB}} file as well as to the current MD simulation. This means that by restarting repeatedly with {{TAG\|ML_MODE\|train}}, and copying the {{FILE\|ML_ABN}} file from the previous run to {{FILE\|ML_AB}}(!), it is possible to iteratively extend the applicability of the a machine learning force field, e.g., by exploring different temperature ranges or element compositions.}}
	</li>		</li>
	</ol>		</ol>
	</li><br />		</li><br />
	<li>		<li>
	{{~~TAGO~~\|ML_MODE\|select}}''': Re-selection of local reference configurations'''<p>		{{TAG\|ML_MODE\|select}}''': Re-selection of local reference configurations'''<p>
	A new machine learning force field is generated from the ''ab initio'' data provided in the {{FILE\|ML_AB}} file. The structures are read and processed one by one as if harvested in an MD simulation. In other words, the same steps are performed as in on-the-fly training but the source of the data are not actual ''ab initio'' calculations in an MD run but the series of structures available in the {{FILE\|ML_AB}} file. The list of local reference configurations on the {{FILE\|ML_AB}} file will be ignored. Instead a new collection of local reference configurations is determined and written to the resulting {{FILE\|ML_ABN}} file.</p>		A new machine learning force field is generated from the ''ab initio'' data provided in the {{FILE\|ML_AB}} file. The structures are read and processed one by one as if harvested in an MD simulation. In other words, the same steps are performed as in on-the-fly training but the source of the data are not actual ''ab initio'' calculations in an MD run but the series of structures available in the {{FILE\|ML_AB}} file. The list of local reference configurations on the {{FILE\|ML_AB}} file will be ignored. Instead a new collection of local reference configurations is determined and written to the resulting {{FILE\|ML_ABN}} file.</p>
	{{NB\|important\|This operation mode allows to generate a {{VASP}} machine learning force field from pre-computed or external ''ab initio'' data sets. In contrast to {{~~TAGO~~\|ML_MODE\|refit}} also the local reference configurations are selected from the entire data set. For example, an {{FILE\|ML_AB}} file created manually by extracting ''ab initio'' data (energies, forces, stresses) from {{FILE\|OUTCAR}} files (or even other external sources) can be processed in this mode without prior knowledge of local reference configurations (only a dummy section must be added to the {{FILE\|ML_AB}} file, see its documentation). Similar to on-the-fly training this mode generates {{FILE\|ML_FFN}} files and {{FILE\|ML_ABN}} '''with''' local reference configurations.}}<p>		{{NB\|important\|This operation mode allows to generate a {{VASP}} machine learning force field from pre-computed or external ''ab initio'' data sets. In contrast to {{TAG\|ML_MODE\|refit}} also the local reference configurations are selected from the entire data set. For example, an {{FILE\|ML_AB}} file created manually by extracting ''ab initio'' data (energies, forces, stresses) from {{FILE\|OUTCAR}} files (or even other external sources) can be processed in this mode without prior knowledge of local reference configurations (only a dummy section must be added to the {{FILE\|ML_AB}} file, see its documentation). Similar to on-the-fly training this mode generates {{FILE\|ML_FFN}} files and {{FILE\|ML_ABN}} '''with''' local reference configurations.}}<p>
	A new iteration through the training structures can lead to a frequent update of the force field. This is quite time-consuming. Hence, for this mode the default value of {{TAG\|ML_CDOUB}} is automatically increased from 2 to 4 which will result in a much less frequent update of the force field. This leads to much more efficient calculations while practically not changing the results.</p>		A new iteration through the training structures can lead to a frequent update of the force field. This is quite time-consuming. Hence, for this mode the default value of {{TAG\|ML_CDOUB}} is automatically increased from 2 to 4 which will result in a much less frequent update of the force field. This leads to much more efficient calculations while practically not changing the results.</p>
	{{NB\|tip\|If calculations for {{~~TAGO~~\|ML_MODE\|select}} are too time-consuming, it is useful to increase {{TAG\|ML_MCONF_NEW}} to values around 10-16. Together with {{~~TAGO~~\|ML_CDOUB\|4}}, this often accelerates the calculations by a factor of 2-4.}}<p>		{{NB\|tip\|If calculations for {{TAG\|ML_MODE\|select}} are too time-consuming, it is useful to increase {{TAG\|ML_MCONF_NEW}} to values around 10-16. Together with {{TAG\|ML_CDOUB\|4}}, this often accelerates the calculations by a factor of 2-4.}}<p>
	The {{FILE\|ML_AB}} file may contain values for <tt>CTIFOR</tt> for each training structure. These are the thresholds used to sample that structure from the previous training. The thresholds found on the {{FILE\|ML_AB}} will be re-used unless a threshold is explicitly specified in the {{FILE\|INCAR}} file, by means of the {{TAG\|ML_CTIFOR}} tag. In the latter case the thresholds from the {{TAG\|ML_AB}} file are ignored. In case the {{FILE\|ML_AB}} contains '''no''' <tt>CTIFOR</tt> information and '''no''' threshold is specified in the {{FILE\|INCAR}} file, the default value for {{TAG\|ML_CTIFOR}} is used.</p><p>		The {{FILE\|ML_AB}} file may contain values for <tt>CTIFOR</tt> for each training structure. These are the thresholds used to sample that structure from the previous training. The thresholds found on the {{FILE\|ML_AB}} will be re-used unless a threshold is explicitly specified in the {{FILE\|INCAR}} file, by means of the {{TAG\|ML_CTIFOR}} tag. In the latter case the thresholds from the {{TAG\|ML_AB}} file are ignored. In case the {{FILE\|ML_AB}} contains '''no''' <tt>CTIFOR</tt> information and '''no''' threshold is specified in the {{FILE\|INCAR}} file, the default value for {{TAG\|ML_CTIFOR}} is used.</p><p>
	This mode automatically sets {{~~TAGO~~\|NSW\|1}} and {{~~TAGO~~\|ML_CDOUB\|4}}.For VASP versions prior to 6.4.0 this corresponds to {{~~TAGO~~\|ML_ISTART\|3}}.		This mode automatically sets {{TAG\|NSW\|1}} and {{TAG\|ML_CDOUB\|4}}.For VASP versions prior to 6.4.0 this corresponds to {{TAG\|ML_ISTART\|3}}.
	{{NB\|warning\|{{~~TAGO~~\|ML_MODE\|select}} ignores the structure in the {{TAG\|POSCAR}} and hence no error, force and stress predictions are made at the end of this calculation (instead zeros are written to stdout, {{TAG\|OSZICAR}} and {{TAG\|OUTCAR}}).}}</p>		{{NB\|warning\|{{TAG\|ML_MODE\|select}} ignores the structure in the {{TAG\|POSCAR}} and hence no error, force and stress predictions are made at the end of this calculation (instead zeros are written to stdout, {{TAG\|OSZICAR}} and {{TAG\|OUTCAR}}).}}</p>
	</li><br />		</li><br />
	<li>{{~~TAGO~~\|ML_MODE\|refit}}''': Refit a force field for "fast" evaluation'''<p>		<li>{{TAG\|ML_MODE\|refit}}''': Refit a force field for "fast" evaluation'''<p>
	Similar to {{~~TAGO~~\|ML_MODE\|select}}, refitting is done based on an existing {{TAG\|ML_AB}} file, but the number of local reference configurations for each species is taken from the {{TAG\|ML_AB}} file. Sparsification is performed on the local reference configurations, so the resulting {{TAG\|ML_ABN}} file will contain the same number or fewer local reference configurations than the {{TAG\|ML_AB}} file.</p><p>		Similar to {{TAG\|ML_MODE\|select}}, refitting is done based on an existing {{TAG\|ML_AB}} file, but the number of local reference configurations for each species is taken from the {{TAG\|ML_AB}} file. Sparsification is performed on the local reference configurations, so the resulting {{TAG\|ML_ABN}} file will contain the same number or fewer local reference configurations than the {{TAG\|ML_AB}} file.</p><p>
	By default the resulting force field is geared towards "fast" evaluation to speed up production runs ({{~~TAGO~~\|ML_LFAST\|.TRUE.}}). This comes at the cost of not being able to evaluate Bayesian error estimates.</p>		By default the resulting force field is geared towards "fast" evaluation to speed up production runs ({{TAG\|ML_LFAST\|.TRUE.}}). This comes at the cost of not being able to evaluate Bayesian error estimates.</p>
	{{NB\|warning\|We strongly advise to use {{~~TAGO~~\|ML_MODE\|refit}} if no Bayesian error estimates are required during production runs.}}<p>		{{NB\|warning\|We strongly advise to use {{TAG\|ML_MODE\|refit}} if no Bayesian error estimates are required during production runs.}}<p>
	This mode automatically sets {{~~TAGO~~\|ML_LFAST\|.TRUE.}}, {{~~TAGO~~\|NSW\|1}}, {{~~TAGO~~\|ML_IALGO_LINREG\|4}}, {{~~TAGO~~\|ML_SIGW0\|1E-7}}, {{~~TAGO~~\|ML_SIGV0\|1}} and {{~~TAGO~~\|ML_EPS_LOW\|1E-11}}. For VASP versions prior to 6.4.0 this corresponds to {{~~TAGO~~\|ML_ISTART\|4}}.</p>		This mode automatically sets {{TAG\|ML_LFAST\|.TRUE.}}, {{TAG\|NSW\|1}}, {{TAG\|ML_IALGO_LINREG\|4}}, {{TAG\|ML_SIGW0\|1E-7}}, {{TAG\|ML_SIGV0\|1}} and {{TAG\|ML_EPS_LOW\|1E-11}}. For VASP versions prior to 6.4.0 this corresponds to {{TAG\|ML_ISTART\|4}}.</p>
	</li><br />		</li><br />
	<li>{{~~TAGO~~\|ML_MODE\|refitbayesian}}''': Refit a force field with Bayesian regression (deprecated)'''<p>		<li>{{TAG\|ML_MODE\|refitbayesian}}''': Refit a force field with Bayesian regression (deprecated)'''<p>
	Same as {{~~TAGO~~\|ML_MODE\|refit}}, but Bayesian regression is employed. This results in lower accuracy and much slower force fields than using {{~~TAGO~~\|ML_MODE\|refit}} and should be used with caution. On the other hand, this mode allows the generation of {{TAG\|ML_FFN}} files that can calculate Bayesian error estimates in addition to predictions.</p><p>		Same as {{TAG\|ML_MODE\|refit}}, but Bayesian regression is employed. This results in lower accuracy and much slower force fields than using {{TAG\|ML_MODE\|refit}} and should be used with caution. On the other hand, this mode allows the generation of {{TAG\|ML_FFN}} files that can calculate Bayesian error estimates in addition to predictions.</p><p>
	This modes sets {{~~TAGO~~\|NSW\|1}}, {{~~TAGO~~\|ML_IALGO_LINREG\|1}} and {{~~TAGO~~\|ML_LFAST\|.FALSE.}}. For VASP versions prior to 6.4.0 this corresponds to {{~~TAGO~~\|ML_ISTART\|4}}.</p>		This modes sets {{TAG\|NSW\|1}}, {{TAG\|ML_IALGO_LINREG\|1}} and {{TAG\|ML_LFAST\|.FALSE.}}. For VASP versions prior to 6.4.0 this corresponds to {{TAG\|ML_ISTART\|4}}.</p>
	</li><br />		</li><br />
	<li>{{~~TAGO~~\|ML_MODE\|run}}''': Perform only force field predictions'''<p>		<li>{{TAG\|ML_MODE\|run}}''': Perform only force field predictions'''<p>
	A previously trained machine learning force field is read from the {{FILE\|ML_FF}} file, and the MD simulation is driven with predictions from the force field only. '''No''' ''ab initio'' calculations are performed and '''no''' learning is executed. This setting is typically used when the machine learning force field is considered mature and ready for production runs.</p><p>		A previously trained machine learning force field is read from the {{FILE\|ML_FF}} file, and the MD simulation is driven with predictions from the force field only. '''No''' ''ab initio'' calculations are performed and '''no''' learning is executed. This setting is typically used when the machine learning force field is considered mature and ready for production runs.</p><p>
	Optionally, if the force field was refitted using {{~~TAGO~~\|ML_MODE\|refitbayesian}}, the Bayesian error estimate of the energies, forces, and stress can be computed and logged in the {{FILE\|ML_LOGFILE}}. The output frequency of the Bayesian errors can be set via the {{TAG\|ML_IERR}} tag, the default is 0.</p><p>		Optionally, if the force field was refitted using {{TAG\|ML_MODE\|refitbayesian}}, the Bayesian error estimate of the energies, forces, and stress can be computed and logged in the {{FILE\|ML_LOGFILE}}. The output frequency of the Bayesian errors can be set via the {{TAG\|ML_IERR}} tag, the default is 0.</p><p>
	For VASP versions prior to 6.4.0 this corresponds to {{~~TAGO~~\|ML_ISTART\|2}}.</p>		For VASP versions prior to 6.4.0 this corresponds to {{TAG\|ML_ISTART\|2}}.</p>
	</li><br />		</li><br />
	<li>{{~~TAGO~~\|ML_MODE\|none}}''': The tag is ignored'''		<li>{{TAG\|ML_MODE\|none}}''': The tag is ignored'''
	</ul><br />		</ul><br />
	{{NB\|warning\|If any option other than the above is chosen or there is a spelling error (be careful to write everything in upper case or lower case letters) the code will exit with an error.}}		{{NB\|warning\|If any option other than the above is chosen or there is a spelling error (be careful to write everything in upper case or lower case letters) the code will exit with an error.}}