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Parametrization of analytic interatomic potential functions using neural networks
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10.1063/1.2957490
/content/aip/journal/jcp/129/4/10.1063/1.2957490
http://aip.metastore.ingenta.com/content/aip/journal/jcp/129/4/10.1063/1.2957490

Figures

Image of FIG. 1.
FIG. 1.

Flow diagram for the operation of the NN procedure for empirical parameter adjustment to a database. is the number of points in the database. See the text for definitions of the remaining variables.

Image of FIG. 2.
FIG. 2.

Flow diagram for the operation of the NN procedure for empirical parameter adjustment to a database for a Tersoff potential with and treated as functions of the two-body interparticle distances. is the number of points in the database, which in this case is 10 202. See the text for definitions of the remaining variables.

Image of FIG. 3.
FIG. 3.

Distribution of errors for Solution-1 in Table I. Total number of points in the database is 10 202. The rms error for the distribution is .

Tables

Generic image for table
Table I.

Parameters for the modified Tersoff potential with parameters and treated as linear functions of the two-body interatomic distance. In all parameters sets, , , and . In all cases, the database is the same 10 202 DFT energies.

Generic image for table
Table II.

Comparison of interpolation accuracy of ab initio energies obtained using various methods for three-, four-, five- and six-body systems.

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/content/aip/journal/jcp/129/4/10.1063/1.2957490
2008-07-30
2014-04-24
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752b84549af89a08dbdd7fdb8b9568b5 journal.articlezxybnytfddd
Scitation: Parametrization of analytic interatomic potential functions using neural networks
http://aip.metastore.ingenta.com/content/aip/journal/jcp/129/4/10.1063/1.2957490
10.1063/1.2957490
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