标题
Machine learning of accurate energy-conserving molecular force fields
作者
关键词
-
出版物
Science Advances
Volume 3, Issue 5, Pages e1603015
出版商
American Association for the Advancement of Science (AAAS)
发表日期
2017-05-06
DOI
10.1126/sciadv.1603015
参考文献
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注意:仅列出部分参考文献,下载原文获取全部文献信息。- Perspective: Machine learning potentials for atomistic simulations
- (2016) Jörg Behler JOURNAL OF CHEMICAL PHYSICS
- Comparing molecules and solids across structural and alchemical space
- (2016) Sandip De et al. PHYSICAL CHEMISTRY CHEMICAL PHYSICS
- Modeling quantum nuclei with perturbed path integral molecular dynamics
- (2016) Igor Poltavsky et al. Chemical Science
- Nonlinear gradient denoising: Finding accurate extrema from inaccurate functional derivatives
- (2015) John C. Snyder et al. INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY
- Gaussian approximation potentials: A brief tutorial introduction
- (2015) Albert P. Bartók et al. INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY
- Machine Learning for Quantum Mechanical Properties of Atoms in Molecules
- (2015) Matthias Rupp et al. Journal of Physical Chemistry Letters
- Machine Learning Predictions of Molecular Properties: Accurate Many-Body Potentials and Nonlocality in Chemical Space
- (2015) Katja Hansen et al. Journal of Physical Chemistry Letters
- Learning scheme to predict atomic forces and accelerate materials simulations
- (2015) V. Botu et al. PHYSICAL REVIEW B
- Molecular Dynamics with On-the-Fly Machine Learning of Quantum-Mechanical Forces
- (2015) Zhenwei Li et al. PHYSICAL REVIEW LETTERS
- i-PI: A Python interface for ab initio path integral molecular dynamics simulations
- (2013) Michele Ceriotti et al. COMPUTER PHYSICS COMMUNICATIONS
- Assessment and Validation of Machine Learning Methods for Predicting Molecular Atomization Energies
- (2013) Katja Hansen et al. Journal of Chemical Theory and Computation
- Machine learning of molecular electronic properties in chemical compound space
- (2013) Grégoire Montavon et al. NEW JOURNAL OF PHYSICS
- On representing chemical environments
- (2013) Albert P. Bartók et al. PHYSICAL REVIEW B
- Construction of high-dimensional neural network potentials using environment-dependent atom pairs
- (2012) K. V. Jovan Jose et al. JOURNAL OF CHEMICAL PHYSICS
- Finding Density Functionals with Machine Learning
- (2012) John C. Snyder et al. PHYSICAL REVIEW LETTERS
- Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning
- (2012) Matthias Rupp et al. PHYSICAL REVIEW LETTERS
- Atom-centered symmetry functions for constructing high-dimensional neural network potentials
- (2011) Jörg Behler JOURNAL OF CHEMICAL PHYSICS
- Neural network potential-energy surfaces in chemistry: a tool for large-scale simulations
- (2011) Jörg Behler PHYSICAL CHEMISTRY CHEMICAL PHYSICS
- Matérn Cross-Covariance Functions for Multivariate Random Fields
- (2010) Tilmann Gneiting et al. JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
- Gaussian Approximation Potentials: The Accuracy of Quantum Mechanics, without the Electrons
- (2010) Albert P. Bartók et al. PHYSICAL REVIEW LETTERS
- Accurate Molecular Van Der Waals Interactions from Ground-State Electron Density and Free-Atom Reference Data
- (2009) Alexandre Tkatchenko et al. PHYSICAL REVIEW LETTERS
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