标题
Recursive evaluation and iterative contraction of N-body equivariant features
作者
关键词
-
出版物
JOURNAL OF CHEMICAL PHYSICS
Volume 153, Issue 12, Pages 121101
出版商
AIP Publishing
发表日期
2020-09-22
DOI
10.1063/5.0021116
参考文献
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- (2020) Anders S. Christensen et al. JOURNAL OF CHEMICAL PHYSICS
- Performance and Cost Assessment of Machine Learning Interatomic Potentials
- (2020) Yunxing Zuo et al. JOURNAL OF PHYSICAL CHEMISTRY A
- Descriptors representing two- and three-body atomic distributions and their effects on the accuracy of machine-learned inter-atomic potentials
- (2020) Ryosuke Jinnouchi et al. JOURNAL OF CHEMICAL PHYSICS
- Gaussian Moments as Physically Inspired Molecular Descriptors for Accurate and Scalable Machine Learning Potentials
- (2020) V. Zaverkin et al. Journal of Chemical Theory and Computation
- Atom-density representations for machine learning
- (2019) Michael J. Willatt et al. JOURNAL OF CHEMICAL PHYSICS
- PhysNet: A Neural Network for Predicting Energies, Forces, Dipole Moments, and Partial Charges
- (2019) Oliver T. Unke et al. Journal of Chemical Theory and Computation
- Incorporating long-range physics in atomic-scale machine learning
- (2019) Andrea Grisafi et al. JOURNAL OF CHEMICAL PHYSICS
- SchNet – A deep learning architecture for molecules and materials
- (2018) K. T. Schütt et al. JOURNAL OF CHEMICAL PHYSICS
- Alchemical and structural distribution based representation for universal quantum machine learning
- (2018) Felix A. Faber 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
- Extending the accuracy of the SNAP interatomic potential form
- (2018) Mitchell A. Wood 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
- Symmetry-Adapted Machine Learning for Tensorial Properties of Atomistic Systems
- (2018) Andrea Grisafi et al. PHYSICAL REVIEW LETTERS
- 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
- 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
- Machine learning unifies the modeling of materials and molecules
- (2017) Albert P. Bartók et al. Science Advances
- Accurate force field for molybdenum by machine learning large materials data
- (2017) Chi Chen et al. PHYSICAL REVIEW MATERIALS
- Communication: Understanding molecular representations in machine learning: The role of uniqueness and target similarity
- (2016) Bing Huang et al. JOURNAL OF CHEMICAL PHYSICS
- Moment Tensor Potentials: A Class of Systematically Improvable Interatomic Potentials
- (2016) Alexander V. Shapeev MULTISCALE MODELING & SIMULATION
- Comparing molecules and solids across structural and alchemical space
- (2016) Sandip De et al. PHYSICAL CHEMISTRY CHEMICAL PHYSICS
- Many Molecular Properties from One Kernel in Chemical Space
- (2015) Raghunathan Ramakrishnan et al. CHIMIA
- Spectral neighbor analysis method for automated generation of quantum-accurate interatomic potentials
- (2015) A.P. Thompson et al. JOURNAL OF COMPUTATIONAL PHYSICS
- Sparse representation for a potential energy surface
- (2014) Atsuto Seko et al. PHYSICAL REVIEW B
- Quantum chemistry structures and properties of 134 kilo molecules
- (2014) Raghunathan Ramakrishnan et al. Scientific Data
- 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
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