Gaussian approximation potentials: Theory, software implementation and application examples
出版年份 2023 全文链接
标题
Gaussian approximation potentials: Theory, software implementation and application examples
作者
关键词
-
出版物
JOURNAL OF CHEMICAL PHYSICS
Volume 159, Issue 17, Pages -
出版商
AIP Publishing
发表日期
2023-11-06
DOI
10.1063/5.0160898
参考文献
相关参考文献
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- (2023) Jan Kloppenburg et al. JOURNAL OF CHEMICAL PHYSICS
- Modeling high-entropy transition metal alloys with alchemical compression
- (2023) Nataliya Lopanitsyna et al. Physical Review Materials
- Massively parallel fitting of Gaussian approximation potentials
- (2023) Sascha Klawohn et al. Machine Learning Science and Technology
- Machine-learned acceleration for molecular dynamics in CASTEP
- (2023) Tamás K. Stenczel et al. JOURNAL OF CHEMICAL PHYSICS
- Tensor-Reduced Atomic Density Representations
- (2023) James P. Darby et al. PHYSICAL REVIEW LETTERS
- Efficient parametrization of the atomic cluster expansion
- (2022) Anton Bochkarev et al. Physical Review Materials
- E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials
- (2022) Simon Batzner et al. Nature Communications
- Accurate Computational Prediction of Core-Electron Binding Energies in Carbon-Based Materials: A Machine-Learning Model Combining Density-Functional Theory and GW
- (2022) Dorothea Golze et al. CHEMISTRY OF MATERIALS
- Compressing local atomic neighbourhood descriptors
- (2022) James P. Darby et al. npj Computational Materials
- Origins of structural and electronic transitions in disordered silicon
- (2021) Volker L. Deringer et al. NATURE
- Experimentally Driven Automated Machine-Learned Interatomic Potential for a Refractory Oxide
- (2021) Ganesh Sivaraman et al. PHYSICAL REVIEW LETTERS
- Physics-Inspired Structural Representations for Molecules and Materials
- (2021) Felix Musil et al. CHEMICAL REVIEWS
- Gaussian Process Regression for Materials and Molecules
- (2021) Volker L. Deringer et al. CHEMICAL REVIEWS
- Optimal radial basis for density-based atomic representations
- (2021) Alexander Goscinski et al. JOURNAL OF CHEMICAL PHYSICS
- Machine learning interatomic potential developed for molecular simulations on thermal properties of β-Ga2O3
- (2020) Yuan-Bin Liu et al. JOURNAL OF CHEMICAL PHYSICS
- Phase Transitions of Hybrid Perovskites Simulated by Machine-Learning Force Fields Trained on the Fly with Bayesian Inference
- (2019) Ryosuke Jinnouchi et al. PHYSICAL REVIEW LETTERS
- SchNet – A deep learning architecture for molecules and materials
- (2018) K. T. Schütt et al. JOURNAL OF CHEMICAL PHYSICS
- Machine learning of molecular properties: Locality and active learning
- (2018) Konstantin Gubaev 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
- Achieving DFT accuracy with a machine-learning interatomic potential: Thermomechanics and defects in bcc ferromagnetic iron
- (2018) Daniele Dragoni et al. PHYSICAL REVIEW MATERIALS
- Reactivity of Amorphous Carbon Surfaces: Rationalizing the Role of Structural Motifs in Functionalization Using Machine Learning
- (2018) Miguel A. Caro et al. CHEMISTRY OF MATERIALS
- Accelerating high-throughput searches for new alloys with active learning of interatomic potentials
- (2018) Konstantin Gubaev et al. COMPUTATIONAL MATERIALS SCIENCE
- Chemical shifts in molecular solids by machine learning
- (2018) Federico M. Paruzzo et al. Nature Communications
- Machine Learning a General-Purpose Interatomic Potential for Silicon
- (2018) Albert P. Bartók et al. Physical Review X
- The atomic simulation environment—a Python library for working with atoms
- (2017) Ask Hjorth Larsen et al. JOURNAL OF PHYSICS-CONDENSED MATTER
- Comparing molecules and solids across structural and alchemical space
- (2016) Sandip De et al. PHYSICAL CHEMISTRY CHEMICAL PHYSICS
- Accuracy and transferability of Gaussian approximation potential models for tungsten
- (2014) Wojciech J. Szlachta et al. PHYSICAL REVIEW B
- Quantum chemistry structures and properties of 134 kilo molecules
- (2014) Raghunathan Ramakrishnan et al. Scientific Data
- Machine-learning approach for one- and two-body corrections to density functional theory: Applications to molecular and condensed water
- (2013) Albert P. Bartók et al. PHYSICAL REVIEW B
- On representing chemical environments
- (2013) Albert P. Bartók 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
- CUR matrix decompositions for improved data analysis
- (2009) Michael W. Mahoney et al. PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
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