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Modified Feed-Forward Neural Network Structures and Combined-Function-Derivative Approximations Incorporating Exchange Symmetry for Potential Energy Surface Fitting

https://doi.org/10.1021/jp3020386

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1002/anie.199305031openalex-snapshot

Neural Networks in Chemistry

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Molecular dynamics investigations of chlorine peroxide dissociation on a neural network ab initio potential energy surface

inferredcites →https://doi.org/10.1016/j.cplett.2004.07.076openalex-snapshot

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Kohonen and counterpropagation artificial neural networks in analytical chemistry

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Evolutionary Optimization in Quantitative Structure−Activity Relationship: An Application of Genetic Neural Networks

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Representation of Intermolecular Potential Functions by Neural Networks

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Tests of halogen photochemistry using in situ measurements of ClO and BrO in the lower polar stratosphere

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Photodissociation dynamics of ClOOCl at 248.4 and 308.4 nm

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Using neural networks, optimized coordinates, and high-dimensional model representations to obtain a vinyl bromide potential surface

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Simultaneous fitting of a potential-energy surface and its corresponding force fields using feedforward neural networks

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Self-consistent molecular orbital methods. XX. A basis set for correlated wave functions

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Contracted Gaussian basis sets for molecular calculations. I. Second row atoms, Z=11–18

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Neural network models of potential energy surfaces

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Neural networks and physical systems with emergent collective computational abilities.

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Descriptions of surface chemical reactions using a neural network representation of the potential-energy surface

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Nonadiabatic effects in the dissociation of oxygen molecules at the Al(111) surface

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Ab initioquality neural-network potential for sodium

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What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.1002/kin.21759openalex-snapshot

Force training neural network potential energy surface models

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Neural network potentials for metals and oxides – First applications to copper clusters at zinc oxide

inferred← citeshttps://doi.org/10.1002/qua.24795openalex-snapshot

Neural network‐based approaches for building high dimensional and quantum dynamics‐friendly potential energy surfaces

inferred← citeshttps://doi.org/10.1016/j.chemphys.2013.09.007openalex-snapshot

Theoretical investigations of BBS (singlet)→BSB (triplet) transformation on a potential energy surface obtained from neural network fitting

inferred← citeshttps://doi.org/10.1016/j.cplett.2015.04.019openalex-snapshot

An implementation of the Levenberg–Marquardt algorithm for simultaneous-energy-gradient fitting using two-layer feed-forward neural networks

inferred← citeshttps://doi.org/10.1021/acs.chemrev.0c00665openalex-snapshot

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inferred← citeshttps://doi.org/10.1021/acs.jctc.7b01045openalex-snapshot

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inferred← citeshttps://doi.org/10.1021/acs.jctc.9b00043openalex-snapshot

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inferred← citeshttps://doi.org/10.1021/acs.jpca.0c05991openalex-snapshot

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inferred← citeshttps://doi.org/10.1021/acs.jpca.1c09719openalex-snapshot

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inferred← citeshttps://doi.org/10.1021/acs.jpca.3c07872openalex-snapshot

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inferred← citeshttps://doi.org/10.1021/acs.jpcc.9b05814openalex-snapshot

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inferred← citeshttps://doi.org/10.1021/prechem.5c00074openalex-snapshot

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inferred← citeshttps://doi.org/10.1063/1.4817187openalex-snapshot

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inferred← citeshttps://doi.org/10.1063/1.4832697openalex-snapshot

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inferred← citeshttps://doi.org/10.1063/1.4887363openalex-snapshot

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inferred← citeshttps://doi.org/10.1063/1.4943869openalex-snapshot

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inferred← citeshttps://doi.org/10.1063/1.4967258openalex-snapshot

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inferred← citeshttps://doi.org/10.1063/1.5053664openalex-snapshot

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inferred← citeshttps://doi.org/10.1063/1.5093908openalex-snapshot

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inferred← citeshttps://doi.org/10.1063/1.5125851openalex-snapshot

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inferred← citeshttps://doi.org/10.1063/1674-0068/cjcp2304040openalex-snapshot

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inferred← citeshttps://doi.org/10.1063/5.0037684openalex-snapshot

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inferred← citeshttps://doi.org/10.1063/5.0191529openalex-snapshot

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inferred← citeshttps://doi.org/10.1063/5.0201241openalex-snapshot

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inferred← citeshttps://doi.org/10.1063/5.0278678openalex-snapshot

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inferred← citeshttps://doi.org/10.1063/5.0348657openalex-snapshot

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inferred← citeshttps://doi.org/10.1080/0144235x.2016.1200347openalex-snapshot

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inferred← citeshttps://doi.org/10.1088/0953-8984/26/18/183001openalex-snapshot

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inferred← citeshttps://doi.org/10.1103/physrevb.92.054113openalex-snapshot

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