- Home
- Publications
- Publication Search
- Publication Details
Title
Atomic energy mapping of neural network potential
Authors
Keywords
-
Journal
Physical Review Materials
Volume 3, Issue 9, Pages -
Publisher
American Physical Society (APS)
Online
2019-09-05
DOI
10.1103/physrevmaterials.3.093802
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- SIMPLE-NN: An efficient package for training and executing neural-network interatomic potentials
- (2019) Kyuhyun Lee et al. COMPUTER PHYSICS COMMUNICATIONS
- SchNet – A deep learning architecture for molecules and materials
- (2018) K. T. Schütt et al. JOURNAL OF CHEMICAL PHYSICS
- wACSF—Weighted atom-centered symmetry functions as descriptors in machine learning potentials
- (2018) M. Gastegger et al. JOURNAL OF CHEMICAL PHYSICS
- Automatic selection of atomic fingerprints and reference configurations for machine-learning potentials
- (2018) Giulio Imbalzano et al. JOURNAL OF CHEMICAL PHYSICS
- Deep Potential Molecular Dynamics: A Scalable Model with the Accuracy of Quantum Mechanics
- (2018) Linfeng Zhang et al. PHYSICAL REVIEW LETTERS
- Toward Reliable and Transferable Machine Learning Potentials: Uniform Training by Overcoming Sampling Bias
- (2018) Wonseok Jeong et al. Journal of Physical Chemistry C
- Towards exact molecular dynamics simulations with machine-learned force fields
- (2018) Stefan Chmiela et al. Nature Communications
- Machine Learning a General-Purpose Interatomic Potential for Silicon
- (2018) Albert P. Bartók et al. Physical Review X
- Study of Li atom diffusion in amorphous Li3PO4 with neural network potential
- (2017) Wenwen Li et al. JOURNAL OF CHEMICAL PHYSICS
- Modeling Segregation on AuPd(111) Surfaces with Density Functional Theory and Monte Carlo Simulations
- (2017) Jacob R. Boes et al. Journal of Physical Chemistry C
- Addressing uncertainty in atomistic machine learning
- (2017) Andrew A. Peterson et al. PHYSICAL CHEMISTRY CHEMICAL PHYSICS
- An implementation of artificial neural-network potentials for atomistic materials simulations: Performance for TiO 2
- (2016) Nongnuch Artrith et al. COMPUTATIONAL MATERIALS SCIENCE
- Amp : A modular approach to machine learning in atomistic simulations
- (2016) Alireza Khorshidi et al. COMPUTER PHYSICS COMMUNICATIONS
- Novel mixture model for the representation of potential energy surfaces
- (2016) Tien Lam Pham et al. JOURNAL OF CHEMICAL PHYSICS
- Permutation invariant potential energy surfaces for polyatomic reactions using atomistic neural networks
- (2016) Brian Kolb et al. JOURNAL OF CHEMICAL PHYSICS
- QCTFF: On the construction of a novel protein force field
- (2015) Paul L. A. Popelier INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY
- Understanding the Composition and Activity of Electrocatalytic Nanoalloys in Aqueous Solvents: A Combination of DFT and Accurate Neural Network Potentials
- (2014) Nongnuch Artrith et al. NANO LETTERS
- Fast Crystallization of the Phase Change Compound GeTe by Large-Scale Molecular Dynamics Simulations
- (2013) Gabriele C. Sosso et al. Journal of Physical Chemistry Letters
- On representing chemical environments
- (2013) Albert P. Bartók et al. PHYSICAL REVIEW B
- 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
- Microscopic Origins of the Anomalous Melting Behavior of Sodium under High Pressure
- (2012) Hagai Eshet et al. PHYSICAL REVIEW LETTERS
- Atom-centered symmetry functions for constructing high-dimensional neural network potentials
- (2011) Jörg Behler JOURNAL OF CHEMICAL PHYSICS
- High-dimensional neural-network potentials for multicomponent systems: Applications to zinc oxide
- (2011) Nongnuch Artrith et al. PHYSICAL REVIEW B
- Energy density in density functional theory: Application to crystalline defects and surfaces
- (2011) Min Yu 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
- Metadynamics Simulations of the High-Pressure Phases of Silicon Employing a High-Dimensional Neural Network Potential
- (2008) Jörg Behler et al. PHYSICAL REVIEW LETTERS
Publish scientific posters with Peeref
Peeref publishes scientific posters from all research disciplines. Our Diamond Open Access policy means free access to content and no publication fees for authors.
Learn MoreBecome a Peeref-certified reviewer
The Peeref Institute provides free reviewer training that teaches the core competencies of the academic peer review process.
Get Started