wACSF—Weighted atom-centered symmetry functions as descriptors in machine learning potentials
出版年份 2018 全文链接
标题
wACSF—Weighted atom-centered symmetry functions as descriptors in machine learning potentials
作者
关键词
-
出版物
JOURNAL OF CHEMICAL PHYSICS
Volume 148, Issue 24, Pages 241709
出版商
AIP Publishing
发表日期
2018-03-15
DOI
10.1063/1.5019667
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。- Spherical harmonics based descriptor for neural network potentials: Structure and dynamics of Au147 nanocluster
- (2017) Shweta Jindal et al. JOURNAL OF CHEMICAL PHYSICS
- The many-body expansion combined with neural networks
- (2017) Kun Yao et al. JOURNAL OF CHEMICAL PHYSICS
- Learning molecular energies using localized graph kernels
- (2017) Grégoire Ferré et al. JOURNAL OF CHEMICAL PHYSICS
- Prediction Errors of Molecular Machine Learning Models Lower than Hybrid DFT Error
- (2017) Felix A. Faber et al. Journal of Chemical Theory and Computation
- Resolving Transition Metal Chemical Space: Feature Selection for Machine Learning and Structure–Property Relationships
- (2017) Jon Paul Janet et al. JOURNAL OF PHYSICAL CHEMISTRY A
- Genetic Optimization of Training Sets for Improved Machine Learning Models of Molecular Properties
- (2017) Nicholas J. Browning et al. Journal of Physical Chemistry Letters
- Machine learning molecular dynamics for the simulation of infrared spectra
- (2017) Michael Gastegger et al. Chemical Science
- ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost
- (2017) J. S. Smith et al. Chemical Science
- Quantum-chemical insights from deep tensor neural networks
- (2017) Kristof T. Schütt et al. Nature Communications
- Bypassing the Kohn-Sham equations with machine learning
- (2017) Felix Brockherde et al. Nature Communications
- Neural network potential for Al-Mg-Si alloys
- (2017) Ryo Kobayashi et al. PHYSICAL REVIEW MATERIALS
- Potential energy surfaces from high fidelity fitting ofab initiopoints: the permutation invariant polynomial - neural network approach
- (2016) Bin Jiang et al. INTERNATIONAL REVIEWS IN PHYSICAL CHEMISTRY
- Comparing the accuracy of high-dimensional neural network potentials and the systematic molecular fragmentation method: A benchmark study for all-trans alkanes
- (2016) Michael Gastegger et al. JOURNAL OF CHEMICAL PHYSICS
- Communication: Understanding molecular representations in machine learning: The role of uniqueness and target similarity
- (2016) Bing Huang et al. JOURNAL OF CHEMICAL PHYSICS
- Perspective: Machine learning potentials for atomistic simulations
- (2016) Jörg Behler JOURNAL OF CHEMICAL PHYSICS
- Design of efficient molecular organic light-emitting diodes by a high-throughput virtual screening and experimental approach
- (2016) Rafael Gómez-Bombarelli et al. NATURE MATERIALS
- Machine learning exciton dynamics
- (2016) Florian Häse et al. Chemical Science
- Learning from the Harvard Clean Energy Project: The Use of Neural Networks to Accelerate Materials Discovery
- (2015) Edward O. Pyzer-Knapp et al. ADVANCED FUNCTIONAL MATERIALS
- Fourier series of atomic radial distribution functions: A molecular fingerprint for machine learning models of quantum chemical properties
- (2015) O. Anatole von Lilienfeld et al. INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY
- Constructing high-dimensional neural network potentials: A tutorial review
- (2015) Jörg Behler INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY
- Gaussian approximation potentials: A brief tutorial introduction
- (2015) Albert P. Bartók et al. INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY
- High-Dimensional Neural Network Potentials for Organic Reactions and an Improved Training Algorithm
- (2015) Michael Gastegger et al. Journal of Chemical Theory and Computation
- 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
- Molecular Dynamics with On-the-Fly Machine Learning of Quantum-Mechanical Forces
- (2015) Zhenwei Li et al. PHYSICAL REVIEW LETTERS
- Representing potential energy surfaces by high-dimensional neural network potentials
- (2014) J Behler JOURNAL OF PHYSICS-CONDENSED MATTER
- How to represent crystal structures for machine learning: Towards fast prediction of electronic properties
- (2014) K. T. Schütt et al. PHYSICAL REVIEW B
- Quantum chemistry structures and properties of 134 kilo molecules
- (2014) Raghunathan Ramakrishnan et al. Scientific Data
- Metrics for measuring distances in configuration spaces
- (2013) Ali Sadeghi et al. JOURNAL OF CHEMICAL PHYSICS
- Permutation invariant polynomial neural network approach to fitting potential energy surfaces
- (2013) Bin Jiang et al. JOURNAL OF CHEMICAL PHYSICS
- Accuracy and tractability of a kriging model of intramolecular polarizable multipolar electrostatics and its application to histidine
- (2013) Shaun M. Kandathil et al. JOURNAL OF COMPUTATIONAL CHEMISTRY
- On representing chemical environments
- (2013) Albert P. Bartók et al. PHYSICAL REVIEW B
- 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
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