ML SION1 - Revision history

Karsai: /* Related tags and articles */

2022-04-08T13:31:27Z

Karsai at 07:42, 7 April 2022

2022-04-07T07:42:47Z

← Older revision		Revision as of 07:42, 7 April 2022
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			{{DISPLAYTITLE:ML_SION1}}
	{{TAGDEF\|ML_SION1\|[real]\|0.5}}		{{TAGDEF\|ML_SION1\|[real]\|0.5}}

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	The unit of {{TAG\|ML_SION1}} is <math>\AA</math>.		The unit of {{TAG\|ML_SION1}} is <math>\AA</math>.

	== Related ~~Tags~~ and ~~Sections~~ ==		== Related tags and articles ==
	{{TAG\|ML_LMLFF}}, {{TAG\|ML_SION2}}, {{TAG\|ML_RCUT1}}, {{TAG\|ML_RCUT2}}, {{TAG\|ML_MRB1}}, {{TAG\|ML_MRB2}}		{{TAG\|ML_LMLFF}}, {{TAG\|ML_SION2}}, {{TAG\|ML_RCUT1}}, {{TAG\|ML_RCUT2}}, {{TAG\|ML_MRB1}}, {{TAG\|ML_MRB2}}

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	----		----

	[[Category:INCAR]][[Category:Machine Learning]][[Category:Machine Learned Force Fields~~]][[Category: Alpha~~]]		[[Category:INCAR tag]][[Category:Machine Learning]][[Category:Machine Learned Force Fields]]

Kresse: Cross reference updated to Accurate force fields

2022-02-16T13:26:39Z

Cross reference updated to Accurate force fields

← Older revision		Revision as of 13:26, 16 February 2022
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	The tag {{TAG\|ML_SION1}} sets the width <math>\sigma_\text{atom}</math> of the above Gaussian function (see [[Machine learning force field: Theory#Descriptors\|this section]] for more details).		The tag {{TAG\|ML_SION1}} sets the width <math>\sigma_\text{atom}</math> of the above Gaussian function (see [[Machine learning force field: Theory#Descriptors\|this section]] for more details).
	{{BOX\|tip\|Our test calculations indicate that {{TAG\|ML_SION1}} {{=}} {{TAG\|ML_SION2}} results in an optimal training performance. Furthermore, a value of 0.5 was found to be a good default value for both. However, the best choice is somewhat system-dependent. For instance, a smaller value for {{TAG\|ML_SION1}} can increase the number of local reference configurations, and hence ultimately the quality of the MLFF. See also [[Machine learning force field: ~~Theory~~#~~Sparsification of local reference configurations~~\|here]].		{{BOX\|tip\|Our test calculations indicate that {{TAG\|ML_SION1}} {{=}} {{TAG\|ML_SION2}} results in an optimal training performance. Furthermore, a value of 0.5 was found to be a good default value for both. However, the best choice is somewhat system-dependent. For instance, a smaller value for {{TAG\|ML_SION1}} can increase the number of local reference configurations, and hence ultimately the quality of the MLFF. See also [[Machine learning force field calculations: Basics#Accurate force fields\|here]].
	}}		}}
	The unit of {{TAG\|ML_SION1}} is <math>\AA</math>.		The unit of {{TAG\|ML_SION1}} is <math>\AA</math>.

Kresse: Cross reference to Sparsification of local reference configurations

2022-02-16T13:25:09Z

Cross reference to Sparsification of local reference configurations

← Older revision		Revision as of 13:25, 16 February 2022
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	The tag {{TAG\|ML_SION1}} sets the width <math>\sigma_\text{atom}</math> of the above Gaussian function (see [[Machine learning force field: Theory#Descriptors\|this section]] for more details).		The tag {{TAG\|ML_SION1}} sets the width <math>\sigma_\text{atom}</math> of the above Gaussian function (see [[Machine learning force field: Theory#Descriptors\|this section]] for more details).
	{{BOX\|tip\|Our test calculations indicate that {{TAG\|ML_SION1}} {{=}} {{TAG\|ML_SION2}} results in an optimal training performance. Furthermore, a value of 0.5 was found to be a good default value for both. However, the best choice is system-dependent, ~~careful testing may improve machine~~ learning ~~results~~.}}		{{BOX\|tip\|Our test calculations indicate that {{TAG\|ML_SION1}} {{=}} {{TAG\|ML_SION2}} results in an optimal training performance. Furthermore, a value of 0.5 was found to be a good default value for both. However, the best choice is somewhat system-dependent. For instance, a smaller value for {{TAG\|ML_SION1}} can increase the number of local reference configurations, and hence ultimately the quality of the MLFF. See also [[Machine learning force field: Theory#Sparsification of local reference configurations\|here]].
			}}
	The unit of {{TAG\|ML_SION1}} is <math>\AA</math>.		The unit of {{TAG\|ML_SION1}} is <math>\AA</math>.

Singraber at 11:37, 13 October 2021

2021-10-13T11:37:54Z

← Older revision		Revision as of 11:37, 13 October 2021
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	{{TAGDEF\|ML_SION1\|[real]\|0.5}}		{{TAGDEF\|ML_SION1\|[real]\|0.5}}

	Description: This tag specifies the width <math>\sigma_\text{atom}</math> of the Gaussian functions used for broadening the atomic distributions of the radial descriptor <math>\rho^{(2)}_i(r)</math> within the machine learning force field method ~~(see [[Machine learning force field: Theory#Descriptors\|this section]])~~.		Description: This tag specifies the width <math>\sigma_\text{atom}</math> of the Gaussian functions used for broadening the atomic distributions of the radial descriptor <math>\rho^{(2)}_i(r)</math> within the machine learning force field method.
	----		----
			The radial descriptor is constructed from

			<math>
			\rho_{i}^{(2)}\left(r\right) = \frac{1}{4\pi} \int \rho_{i}\left(r\hat{\mathbf{r}}\right) d\hat{\mathbf{r}}, \quad \text{where} \quad
			\rho_{i}\left(\mathbf{r}\right) = \sum\limits_{j=1}^{N_{\mathrm{a}}} f_{\mathrm{cut}}\left(r_{ij}\right) g\left(\mathbf{r}-\mathbf{r}_{ij}\right)
			</math>

			and <math>g\left(\mathbf{r}\right)</math> is the following approximation of the delta function:

			<math>
			g\left(\mathbf{r}\right)=\frac{1}{\sqrt{2\sigma_{\mathrm{atom}}\pi}}\mathrm{exp}\left(-\frac{\|\mathbf{r}\|^{2}}{2\sigma_{\mathrm{atom}}^{2}}\right).
			</math>

			The tag {{TAG\|ML_SION1}} sets the width <math>\sigma_\text{atom}</math> of the above Gaussian function (see [[Machine learning force field: Theory#Descriptors\|this section]] for more details).
			{{BOX\|tip\|Our test calculations indicate that {{TAG\|ML_SION1}} {{=}} {{TAG\|ML_SION2}} results in an optimal training performance. Furthermore, a value of 0.5 was found to be a good default value for both. However, the best choice is system-dependent, careful testing may improve machine learning results.}}
	The unit of {{TAG\|ML_SION1}} is <math>\AA</math>.		The unit of {{TAG\|ML_SION1}} is <math>\AA</math>.
	{{BOX\|note\|Our test calculations indicate that {{TAG\|ML_SION1}} {{=}} {{TAG\|ML_SION2}} results in an optimal training performance. Furthermore, a value of 0.5 was found to be a good default value for both. However, the best choice is system-dependent, careful testing may improve machine learning results.}}

	== Related Tags and Sections ==		== Related Tags and Sections ==

Singraber at 20:38, 10 October 2021

2021-10-10T20:38:38Z

← Older revision		Revision as of 20:38, 10 October 2021
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	{{TAGDEF\|ML_SION1\|[real]\|0.5}}		{{TAGDEF\|ML_SION1\|[real]\|0.5}}

	Description: This tag specifies the width <math>\sigma_\text{atom}</math> of the Gaussian functions used for broadening the atomic distributions ~~for~~ the radial descriptor <math>\rho^{(2)}_i(r)</math> within the machine learning force field method (see [[Machine learning force field: Theory#Descriptors\|this section]]).		Description: This tag specifies the width <math>\sigma_\text{atom}</math> of the Gaussian functions used for broadening the atomic distributions of the radial descriptor <math>\rho^{(2)}_i(r)</math> within the machine learning force field method (see [[Machine learning force field: Theory#Descriptors\|this section]]).
	----		----

Singraber at 20:24, 10 October 2021

2021-10-10T20:24:36Z

← Older revision		Revision as of 20:24, 10 October 2021
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	{{TAGDEF\|ML_SION1\|[real]\|0.~~333333~~}}		{{TAGDEF\|ML_SION1\|[real]\|0.5}}

	Description: This tag specifies the width <math>\sigma_\text{atom}</math> of the Gaussian functions used for broadening the atomic distributions for the radial descriptor <math>\rho^{(2)}_i(r)</math> within the machine learning force field method (see [[Machine learning force field: Theory#Descriptors\|this section]]).		Description: This tag specifies the width <math>\sigma_\text{atom}</math> of the Gaussian functions used for broadening the atomic distributions for the radial descriptor <math>\rho^{(2)}_i(r)</math> within the machine learning force field method (see [[Machine learning force field: Theory#Descriptors\|this section]]).
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	The unit of {{TAG\|ML_SION1}} is <math>\AA</math>.		The unit of {{TAG\|ML_SION1}} is <math>\AA</math>.
	{{BOX\|~~tip~~\|Our test calculations indicate that ~~a ratio~~ {{TAG\|~~ML_SION2~~}} / {{~~TAG\|ML_SION1~~}} {{=}} ~~1.5~~ results in an optimal training performance. Furthermore, a value of 0.5 was found to be a good default value for ~~{{TAG\|ML_SION2}}~~. ~~Both findings together result in~~ the ~~above default value for {{TAG\|ML_SION1}}~~.~~\|Background:~~}}		{{BOX\|note\|Our test calculations indicate that {{TAG\|ML_SION1}} {{=}} {{TAG\|ML_SION2}} results in an optimal training performance. Furthermore, a value of 0.5 was found to be a good default value for both. However, the best choice is system-dependent, careful testing may improve machine learning results.}}

	== Related Tags and Sections ==		== Related Tags and Sections ==

Singraber at 19:54, 10 October 2021

2021-10-10T19:54:58Z

← Older revision		Revision as of 19:54, 10 October 2021
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	----		----

	The unit of {{TAG\|ML_SION1}} is in <math>\AA</math>.		The unit of {{TAG\|ML_SION1}} is <math>\AA</math>.
	~~Test~~ calculations ~~showed~~ that a 1.5 ~~smaller~~ value ~~for the broadening~~ of ~~the radial descriptor compared~~ to ~~the angular descriptor (see~~ {{TAG\|ML_SION2}}~~) gives optimal results~~.		{{BOX\|tip\|Our test calculations indicate that a ratio {{TAG\|ML_SION2}} / {{TAG\|ML_SION1}} {{=}} 1.5 results in an optimal training performance. Furthermore, a value of 0.5 was found to be a good default value for {{TAG\|ML_SION2}}. Both findings together result in the above default value for {{TAG\|ML_SION1}}.\|Background:}}
	~~The~~ default value for {{TAG\|ML_SION1}} ~~is chosen such that {{TAG~~\|~~ML_SION2~~}} ~~becomes 0.5.~~

	== Related Tags and Sections ==		== Related Tags and Sections ==

Singraber at 18:28, 10 October 2021

2021-10-10T18:28:52Z

← Older revision		Revision as of 18:28, 10 October 2021
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	{{TAGDEF\|ML_SION1\|[real]\|0.333333}}		{{TAGDEF\|ML_SION1\|[real]\|0.333333}}

	Description: This tag specifies the width of the Gaussian functions used for broadening the atomic distributions for the radial descriptor within the machine learning force field method.		Description: This tag specifies the width <math>\sigma_\text{atom}</math> of the Gaussian functions used for broadening the atomic distributions for the radial descriptor <math>\rho^{(2)}_i(r)</math> within the machine learning force field method (see [[Machine learning force field: Theory#Descriptors\|this section]]).
	----		----

Karsai at 11:05, 6 September 2021

2021-09-06T11:05:08Z

← Older revision		Revision as of 11:05, 6 September 2021
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	{{TAGDEF\|~~ML_SION1_MB~~\|[real]\|0.333333}}		{{TAGDEF\|ML_SION1\|[real]\|0.333333}}

	Description: This tag specifies the width of the Gaussian functions used for broadening the atomic distributions for the radial descriptor within the machine learning force field method.		Description: This tag specifies the width of the Gaussian functions used for broadening the atomic distributions for the radial descriptor within the machine learning force field method.

← Older revision		Revision as of 13:25, 16 February 2022
Line 17:		Line 17:

	The tag {{TAG\|ML_SION1}} sets the width <math>\sigma_\text{atom}</math> of the above Gaussian function (see [[Machine learning force field: Theory#Descriptors\|this section]] for more details).		The tag {{TAG\|ML_SION1}} sets the width <math>\sigma_\text{atom}</math> of the above Gaussian function (see [[Machine learning force field: Theory#Descriptors\|this section]] for more details).
	{{BOX\|tip\|Our test calculations indicate that {{TAG\|ML_SION1}} {{=}} {{TAG\|ML_SION2}} results in an optimal training performance. Furthermore, a value of 0.5 was found to be a good default value for both. However, the best choice is system-dependent, ~~careful testing may improve machine~~ learning ~~results~~.}}		{{BOX\|tip\|Our test calculations indicate that {{TAG\|ML_SION1}} {{=}} {{TAG\|ML_SION2}} results in an optimal training performance. Furthermore, a value of 0.5 was found to be a good default value for both. However, the best choice is somewhat system-dependent. For instance, a smaller value for {{TAG\|ML_SION1}} can increase the number of local reference configurations, and hence ultimately the quality of the MLFF. See also [[Machine learning force field: Theory#Sparsification of local reference configurations\|here]].
			}}
	The unit of {{TAG\|ML_SION1}} is <math>\AA</math>.		The unit of {{TAG\|ML_SION1}} is <math>\AA</math>.

← Older revision		Revision as of 13:31, 8 April 2022
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	----		----

	[[Category:INCAR tag]][[Category:Machine ~~Learning]][[Category:Machine Learned Force Fields~~]]		[[Category:INCAR tag]][[Category:Machine-learned force fields]]