Representing potential energy surfaces by high-dimensional neural network potentials.
about
How van der Waals interactions determine the unique properties of waterAccurate Neural Network Description of Surface Phonons in Reactive Gas-Surface Dynamics: N2 + Ru(0001).Crystal Nucleation in Liquids: Open Questions and Future Challenges in Molecular Dynamics Simulations.Energy-free machine learning force field for aluminum.First Principles Neural Network Potentials for Reactive Simulations of Large Molecular and Condensed Systems.Neural network potentials for dynamics and thermodynamics of gold nanoparticles.Perspective: Machine learning potentials for atomistic simulations.Vibrational energy transfer near a dissociative adsorption transition state: State-to-state study of HCl collisions at Au(111).Discovering charge density functionals and structure-property relationships with PROPhet: A general framework for coupling machine learning and first-principles methods.Machine learning molecular dynamics for the simulation of infrared spectra.Material discovery by combining stochastic surface walking global optimization with a neural network† †Electronic supplementary information (ESI) available: Derivation for the gradient of J σ with respect to NN parameters. DFT calculation setups. PaMaterial discovery by combining stochastic surface walking global optimization with a neural network.Efficient and accurate evaluation of potential energy matrix elements for quantum dynamics using Gaussian process regression.A rigorous full-dimensional quantum dynamics study of tunneling splitting of rovibrational states of vinyl radical C2H3.Computational Chemistry: The Fate of Current Methods and Future Challenges.c-T phase diagram and Landau free energies of (AgAu)55 nanoalloy via neural-network molecular dynamic simulations.Local-metrics error-based Shepard interpolation as surrogate for highly non-linear material models in high dimensions.Permutation-invariant distance between atomic configurations.High-dimensional neural network potentials for solvation: The case of protonated water clusters in helium.Comparing the accuracy of high-dimensional neural network potentials and the systematic molecular fragmentation method: A benchmark study for all-trans alkanes.Stratified construction of neural network based interatomic models for multicomponent materialsComputerchemie: das Schicksal aktueller Methoden und zukünftige HerausforderungenHigh order path integrals made easyPotential energy surfaces from high fidelity fitting ofab initiopoints: the permutation invariant polynomial - neural network approach
P2860
Q28828278-EB5F425A-54EA-4F87-901D-673769F9957EQ30355908-07A10677-C347-4B23-B18E-170207BE9FD0Q31102557-B5FE5A58-A91D-4796-8B38-C530ECB80AF7Q38619070-F838FFA6-3E14-4C01-A693-AA817057D8DFQ38777403-20FA0885-C5AB-41B3-9F9F-049C88C698CCQ38935361-C50AD1E4-3AAB-4092-BA7E-00F3163D03A5Q39204283-95B381FB-351C-473F-B253-B63EEC747E3EQ39517084-97C941DA-A5B4-4D7A-AD73-EA9D6642BF2AQ41907039-EF52A2CA-0E30-4C56-A832-284F627D1B4FQ45943844-775FF262-5FBC-4408-8B76-89D01805DD70Q47134340-C5CB9D34-BD41-4B35-95F5-9F073190D143Q47200059-F04E451F-E9CD-4D81-9846-CEA77FADFDC8Q47385754-F31EAC34-E842-4B1B-9DCA-906911CCE803Q47600061-209392D9-41CD-43DB-9DA0-C137EF592164Q47857870-6040768B-F9DA-4859-AFC4-2D9424435ACAQ48051097-8F1E8078-952E-47BC-90EE-18AF5EA0726BQ48374291-1055121B-8959-45C4-BDAF-A53B96D6F9DAQ50561248-7BE3F614-F878-4B8A-BFE6-81975DB7B6EFQ52354279-DCE4BAE7-7278-4004-B708-1494B8A4734FQ53967485-6D76DAA7-BD64-43F6-954D-A809288E023AQ57534305-651C6D26-C334-405E-A05F-84185B375DDDQ57742416-270BFF20-2C6F-4171-9285-54BAA2DF9B16Q58188134-104E1E9B-CB9F-462B-A1F7-8831CA1C9544Q58280316-D3728464-AC9A-4012-90A4-A800601614F0
P2860
Representing potential energy surfaces by high-dimensional neural network potentials.
description
article científic
@ca
article scientifique
@fr
articol științific
@ro
articolo scientifico
@it
artigo científico
@gl
artigo científico
@pt
artigo científico
@pt-br
artikel ilmiah
@id
artikull shkencor
@sq
artículo científico
@es
name
Representing potential energy surfaces by high-dimensional neural network potentials.
@en
type
label
Representing potential energy surfaces by high-dimensional neural network potentials.
@en
prefLabel
Representing potential energy surfaces by high-dimensional neural network potentials.
@en
P356
P1476
Representing potential energy surfaces by high-dimensional neural network potentials.
@en
P304
P356
10.1088/0953-8984/26/18/183001
P50
P577
2014-04-23T00:00:00Z