Parametrization of analytic interatomic potential functions using neural networks. (Q47414537)

15 January 2018

title

Parametrization of analytic interatomic potential functions using neural networks. (English)

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author

Satish T S Bukkapatnam

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ORCID Public Data File 2021

author name string

M Malshe

1

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R Narulkar

2

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L M Raff

3

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M Hagan

4

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S Bukkapatnam

5

1 reference

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R Komanduri

6

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language of work or name

English

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publication date

1 July 2008

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Journal of Chemical Physics

published in

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volume

129

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issue

4

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page(s)

044111

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describes a project that uses

artificial neural network

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based on heuristic

inferred from title

cites work

Unified Approach for Molecular Dynamics and Density-Functional Theory

1 reference

Effective force fields for condensed phase systems from ab initio molecular dynamics simulation: a new method for force-matching.

21 January 2018

1 reference

Training feedforward networks with the Marquardt algorithm.

21 January 2018

1 reference

Interpolating moving least-squares methods for fitting potential energy surfaces: computing high-density potential energy surface data from low-density ab initio data points

21 January 2018

1 reference

Neural networks as a tool for compact representation of ab initio molecular potential energy surfaces.

21 January 2018

1 reference

Ab initio potential-energy surfaces for complex, multichannel systems using modified novelty sampling and feedforward neural networks

21 January 2018

1 reference

Molecular dynamics investigations of the dissociation of SiO2 on an ab initio potential energy surface obtained using neural network methods.

21 January 2018

1 reference

Cis-->trans, trans-->cis isomerizations and N-O bond dissociation of nitrous acid (HONO) on an ab initio potential surface obtained by novelty sampling and feed-forward neural network fitting.

21 January 2018

1 reference

Theoretical investigation of the dissociation dynamics of vibrationally excited vinyl bromide on an ab initio potential-energy surface obtained using modified novelty sampling and feedforward neural networks. II. Numerical application of the method

21 January 2018

1 reference

Modeling solid-state chemistry: Interatomic potentials for multicomponent systems

21 January 2018

1 reference

Multilayer feedforward networks are universal approximators

21 January 2018

1 reference

21 January 2018

Identifiers

DOI

10.1063/1.2957490

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15 January 2018

PubMed ID

18681638

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15 January 2018