Construction of high-dimensional neural network potentials using environment-dependent atom pairs
Published 2012 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
Construction of high-dimensional neural network potentials using environment-dependent atom pairs
Authors
Keywords
-
Journal
JOURNAL OF CHEMICAL PHYSICS
Volume 136, Issue 19, Pages 194111
Publisher
AIP Publishing
Online
2012-05-20
DOI
10.1063/1.4712397
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- A neural network potential-energy surface for the water dimer based on environment-dependent atomic energies and charges
- (2012) Tobias Morawietz et al. JOURNAL OF CHEMICAL PHYSICS
- High-dimensional neural network potentials for metal surfaces: A prototype study for copper
- (2012) Nongnuch Artrith et al. PHYSICAL REVIEW B
- Polarisable multipolar electrostatics from the machine learning method Kriging: an application to alanine
- (2012) Matthew J. L. Mills et al. THEORETICAL CHEMISTRY ACCOUNTS
- Atom-centered symmetry functions for constructing high-dimensional neural network potentials
- (2011) Jörg Behler JOURNAL OF CHEMICAL PHYSICS
- Nucleation mechanism for the direct graphite-to-diamond phase transition
- (2011) Rustam Z. Khaliullin et al. NATURE MATERIALS
- Neural network potential-energy surfaces in chemistry: a tool for large-scale simulations
- (2011) Jörg Behler PHYSICAL CHEMISTRY CHEMICAL PHYSICS
- High-dimensional neural-network potentials for multicomponent systems: Applications to zinc oxide
- (2011) Nongnuch Artrith et al. PHYSICAL REVIEW B
- Intramolecular polarisable multipolar electrostatics from the machine learning method Kriging
- (2011) Matthew J.L. Mills et al. Computational and Theoretical Chemistry
- Potential Energy Surfaces Fitted by Artificial Neural Networks
- (2010) Chris M. Handley et al. JOURNAL OF PHYSICAL CHEMISTRY A
- Ab initioquality neural-network potential for sodium
- (2010) Hagai Eshet et al. PHYSICAL REVIEW B
- Graphite-diamond phase coexistence study employing a neural-network mapping of theab initiopotential energy surface
- (2010) Rustam Z. Khaliullin et al. PHYSICAL REVIEW B
- Signatures of nonadiabaticO2dissociation at Al(111): First-principles fewest-switches study
- (2010) Christian Carbogno et al. PHYSICAL REVIEW B
- Gaussian Approximation Potentials: The Accuracy of Quantum Mechanics, without the Electrons
- (2010) Albert P. Bartók et al. PHYSICAL REVIEW LETTERS
- Ab initio molecular simulations with numeric atom-centered orbitals
- (2009) Volker Blum et al. COMPUTER PHYSICS COMMUNICATIONS
- Development of generalized potential-energy surfaces using many-body expansions, neural networks, and moiety energy approximations
- (2009) M. Malshe et al. JOURNAL OF CHEMICAL PHYSICS
- Optimal construction of a fast and accurate polarisable water potential based on multipole moments trained by machine learning
- (2009) Chris M. Handley et al. PHYSICAL CHEMISTRY CHEMICAL PHYSICS
- Using neural networks, optimized coordinates, and high-dimensional model representations to obtain a vinyl bromide potential surface
- (2008) Sergei Manzhos et al. JOURNAL OF CHEMICAL PHYSICS
- Beyond Point Charges: Dynamic Polarization from Neural Net Predicted Multipole Moments
- (2008) Michael G. Darley et al. Journal of Chemical Theory and Computation
- Mapping Potential Energy Surfaces by Neural Networks: The ethanol/Au(111) interface
- (2008) Diogo A.R.S. Latino et al. JOURNAL OF ELECTROANALYTICAL CHEMISTRY
- Silicon potentials investigated using density functional theory fitted neural networks
- (2008) E Sanville et al. JOURNAL OF PHYSICS-CONDENSED MATTER
- Pressure-induced phase transitions in silicon studied by neural network-based metadynamics simulations
- (2008) Jörg Behler et al. PHYSICA STATUS SOLIDI B-BASIC SOLID STATE PHYSICS
- Nonadiabatic effects in the dissociation of oxygen molecules at the Al(111) surface
- (2008) Jörg Behler et al. PHYSICAL REVIEW B
- Fingerprints for Spin-Selection Rules in the Interaction Dynamics ofO2at Al(111)
- (2008) Christian Carbogno et al. PHYSICAL REVIEW LETTERS
- Metadynamics Simulations of the High-Pressure Phases of Silicon Employing a High-Dimensional Neural Network Potential
- (2008) Jörg Behler et al. PHYSICAL REVIEW LETTERS
Find Funding. Review Successful Grants.
Explore over 25,000 new funding opportunities and over 6,000,000 successful grants.
ExploreBecome a Peeref-certified reviewer
The Peeref Institute provides free reviewer training that teaches the core competencies of the academic peer review process.
Get Started