The fitting of potential energy surfaces using neural networks. Application to the study of the photodissociation processes

Prudente, Frederico V.; Neto, J. J. Soares

doi:10.1016/s0009-2614(98)00207-3

Cited by 61 publications

(57 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Multilayer NNs of the form of Eq. (2) without such architecture constraints have been used for potential fitting, [38,39,41] but there is no evidence that they have advantages over SHL networks in terms of accuracy or fitting cost.…”

Section: Nn Representations Of Pess Neural Networkmentioning

confidence: 99%

“…[31,32] When a simple NN is used the fitting procedure is straightforward. [23,[37][38][39][40][41][42] Fitting to sums of NNs, that is, increasing the complexity of the network, is advantageous when either high accuracy is required or the density of the fitting points is low (often due to high dimensionality). The purpose of the present review, in contrast to the available reviews, is not to list examples of NN PESs but to describe more complex NN methods that two of the authors (S. M. and T. C.) developed and specifically, their evolution from simple NNs toward more complex structures.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Manzhos

Dawes

Carrington

2014

Int J of Quantum Chemistry

198

148

View full text Add to dashboard Cite

Development and applications of neural network (NN)-based approaches for representing potential energy surfaces (PES) of bound and reactive molecular systems are reviewed. Specifically, it is shown that when the density of ab initio points is low, NNs-based potentials with multibody or multimode structure are advantageous for representing high-dimensional PESs. Importantly, with an appropriate choice of the neuron activation function, PESs in the sum-of-products form are naturally obtained, thus addressing a bottleneck problem in quantum dynamics. The use of NN committees is also analyzed and it is shown that while they are able to reduce the fitting error, the reduction is limited by the nonrandom nature of the fitting error. The approaches described here are expected to be directly applicable in other areas of science and engineering where a functional form needs to be constructed in an unbiased way from sparse data.

show abstract

Section: Nn Representations Of Pess Neural Networkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Manzhos

Dawes

Carrington

2014

Int J of Quantum Chemistry

198

148

View full text Add to dashboard Cite

show abstract

“…Then EnsFFNNs were trained with different number of networks (1,5,10,15,20,25,50,75,100,150,200). Fig.…”

Section: Impact Of the Number Of Hidden Neurons And Network In The Ementioning

confidence: 99%

“…In the last years, Neural Networks (NNs) turned out as an alternative way for mapping PES from ab initio/DFT energy data sets. [4][5][6][7][8][9][10][11][12][13][14][15] In such approximation, there are no a priori guesses of analytical functions and the results come out in tabular form. Moreover, once the networks are well trained, they are able to produce, as output, any required number of energy points for numerical interpolations with similar or better accuracy than other representation methods.…”

Section: Introductionmentioning

confidence: 99%

Mapping Potential Energy Surfaces by Neural Networks: The ethanol/Au(111) interface

Latino¹,

Fartaria²,

Freitas³

et al. 2008

Journal of Electroanalytical Chemistry

View full text Add to dashboard Cite

“…NNs have been used for about two decades to construct PESs for a number of different systems and several reviews have been published [22][23][24][25]. Most of these NN potentials, however, are restricted to small molecules [26][27][28][29][30][31][32] or small molecules interacting with frozen metal surfaces [33][34][35][36][37][38]. Only a few potentials for higher-dimensional systems exist, which aim to describe the properties of solids [39,40].…”

Section: Introductionmentioning

confidence: 99%

A Full-Dimensional Neural Network Potential-Energy Surface for Water Clusters up to the Hexamer

Morawietz

Behler

2013

Zeitschrift Für Physikalische Chemie

View full text Add to dashboard Cite

Water clusters have attracted a lot of attention as prototype systems to study hydrogen bonded molecular aggregates but also to gain deeper insights into the properties of liquid water, the solvent of life. All these studies depend on an accurate description of the atomic interactions and countless potentials have been proposed in the literature in the past decades to represent the potential-energy surface (PES) of water. Many of these potentials employ drastic approximations like rigid water monomers and fixed point charges, while on the other hand also several attempts have been made to derive very accurate PESs by fitting data obtained in high-level electronic structure calculations. In recent years artificial neural networks (NNs) have been established as a powerful tool to construct high-dimensional PESs of a variety of systems, but to date no full-dimensional NN PES for water has been reported. Here, we present NN potentials for water clusters containing two to six water molecules trained to density functional theory (DFT) data employing two different exchange-correlation functionals, PBE and RPBE. In contrast to other potentials fitted to first principles data, these NN potentials are not based on a truncated many-body expansion of the energy but consider the interactions between all water molecules explicitly. For both functionals an excellent agreement with the underlying DFT calculations has been found with binding energy errors of only about 1%.

show abstract

The fitting of potential energy surfaces using neural networks. Application to the study of the photodissociation processes

Cited by 61 publications

References 18 publications

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

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

Mapping Potential Energy Surfaces by Neural Networks: The ethanol/Au(111) interface

A Full-Dimensional Neural Network Potential-Energy Surface for Water Clusters up to the Hexamer

Contact Info

Product

Resources

About