(Q39204283)

10 September 2017

title

Perspective: Machine learning potentials for atomistic simulations (English)

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Erratum: "Perspective: Machine learning potentials for atomistic simulations" [J. Chem. Phys. 145, 170901 (2016)].

10 September 2017

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corrigendum / erratum

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12 July 2019

http://api.crossref.org/works?select=update-to&filter=doi:10.1063/1.4971792

Jörg Behler

1

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language of work or name

English

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publication date

1 November 2016

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Journal of Chemical Physics

10 September 2017

published in

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10 September 2017

volume

145

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10 September 2017

issue

17

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10 September 2017

page(s)

170901

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Pattern recognition strategies for molecular surfaces: III. Binding site prediction with a neural network

10 September 2017

cites work

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On the use of neural network ensembles in QSAR and QSPR.

21 January 2018

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Accelerating materials property predictions using machine learning

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Big data of materials science: critical role of the descriptor.

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Crystal fingerprint space--a novel paradigm for studying crystal-structure sets.

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Representing molecule-surface interactions with symmetry-adapted neural networks.

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Modeling solid-state chemistry: Interatomic potentials for multicomponent systems

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Representing potential energy surfaces by high-dimensional neural network potentials

21 January 2018

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Construction of high-dimensional neural network potentials using environment-dependent atom pairs.

21 January 2018

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Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning

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Machine Learning Predictions of Molecular Properties: Accurate Many-Body Potentials and Nonlocality in Chemical Space

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Potential energy surfaces fitted by artificial neural networks.

21 January 2018

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Neural network potential-energy surfaces in chemistry: a tool for large-scale simulations

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Machine learning for quantum mechanics in a nutshell

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A logical calculus of the ideas immanent in nervous activity

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Neural networks for local structure detection in polymorphic systems

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Molecular dynamics with on-the-fly machine learning of quantum-mechanical forces.

21 January 2018

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Optimal construction of a fast and accurate polarisable water potential based on multipole moments trained by machine learning.

21 January 2018

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Support vector machine regression (LS-SVM) - an alternative to artificial neural networks (ANNs) for the analysis of quantum chemistry data?

21 January 2018

1 reference

Approximation by superpositions of a sigmoidal function

21 January 2018

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Approximation capabilities of multilayer feedforward networks

21 January 2018

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Comparing molecules and solids across structural and alchemical space.

21 January 2018

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A fingerprint based metric for measuring similarities of crystalline structures.

21 January 2018

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How van der Waals interactions determine the unique properties of water

21 January 2018

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Concentration-Dependent Proton Transfer Mechanisms in Aqueous NaOH Solutions: From Acceptor-Driven to Donor-Driven and Back.

21 January 2018

1 reference

Understanding the composition and activity of electrocatalytic nanoalloys in aqueous solvents: a combination of DFT and accurate neural network potentials.

21 January 2018

1 reference

Machine Learning for Quantum Mechanical Properties of Atoms in Molecules

21 January 2018

1 reference

Neural network molecular dynamics simulations of solid-liquid interfaces: water at low-index copper surfaces.

21 January 2018

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New opportunities for materials informatics: Resources and data mining techniques for uncovering hidden relationships

21 January 2018

1 reference

Next generation interatomic potentials for condensed systems

21 January 2018

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21 January 2018

Identifiers

DOI

10.1063/1.4966192

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10 September 2017

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