A unified picture of the covalent bond within quantum-accurate force fields: From organic molecules to metallic complexes’ reactivity
出版年份 2019 全文链接
标题
A unified picture of the covalent bond within quantum-accurate force fields: From organic molecules to metallic complexes’ reactivity
作者
关键词
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出版物
Science Advances
Volume 5, Issue 5, Pages eaaw2210
出版商
American Association for the Advancement of Science (AAAS)
发表日期
2019-06-01
DOI
10.1126/sciadv.aaw2210
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。- SchNet – A deep learning architecture for molecules and materials
- (2018) K. T. Schütt et al. JOURNAL OF CHEMICAL PHYSICS
- Guest Editorial: Special Topic on Data-Enabled Theoretical Chemistry
- (2018) Matthias Rupp 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
- The TensorMol-0.1 model chemistry: a neural network augmented with long-range physics
- (2018) Kun Yao et al. Chemical Science
- Machine learning for molecular and materials science
- (2018) Keith T. Butler et al. NATURE
- Towards exact molecular dynamics simulations with machine-learned force fields
- (2018) Stefan Chmiela et al. Nature Communications
- Neural network potentials for dynamics and thermodynamics of gold nanoparticles
- (2017) Siva Chiriki et al. JOURNAL OF CHEMICAL PHYSICS
- ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost
- (2017) J. S. Smith et al. Chemical Science
- Machine learning unifies the modeling of materials and molecules
- (2017) Albert P. Bartók et al. Science Advances
- Machine learning of accurate energy-conserving molecular force fields
- (2017) Stefan Chmiela et al. Science Advances
- A universal strategy for the creation of machine learning-based atomistic force fields
- (2017) Tran Doan Huan et al. npj Computational Materials
- Why is Ferrocene so Exceptional?
- (2016) Didier Astruc EUROPEAN JOURNAL OF INORGANIC CHEMISTRY
- Perspective: Machine learning potentials for atomistic simulations
- (2016) Jörg Behler JOURNAL OF CHEMICAL PHYSICS
- The ReaxFF reactive force-field: development, applications and future directions
- (2016) Thomas P Senftle et al. npj Computational Materials
- 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
- Efficient global optimization of reactive force-field parameters
- (2015) Mark Dittner et al. JOURNAL OF COMPUTATIONAL CHEMISTRY
- Spectral neighbor analysis method for automated generation of quantum-accurate interatomic potentials
- (2015) A.P. Thompson et al. JOURNAL OF COMPUTATIONAL PHYSICS
- Development and Application of a Nonbonded Cu2+ Model That Includes the Jahn–Teller Effect
- (2015) Qinghua Liao 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
- Adaptive machine learning framework to accelerateab initiomolecular dynamics
- (2014) Venkatesh Botu et al. INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY
- An Angular Overlap Model for Cu(II) Ion in the AMOEBA Polarizable Force Field
- (2013) Jin Yu Xiang et al. Journal of Chemical Theory and Computation
- The high-throughput highway to computational materials design
- (2013) Stefano Curtarolo et al. NATURE MATERIALS
- Electronic Structure, Spin-States, and Spin-Crossover Reaction of Heme-Related Fe-Porphyrins: A Theoretical Perspective
- (2012) Md. Ehesan Ali et al. JOURNAL OF PHYSICAL CHEMISTRY B
- Neural network interatomic potential for the phase change material GeTe
- (2012) Gabriele C. Sosso et al. PHYSICAL REVIEW B
- The ORCA program system
- (2011) Frank Neese Wiley Interdisciplinary Reviews-Computational Molecular Science
- A consistent and accurate ab initio parametrization of density functional dispersion correction (DFT-D) for the 94 elements H-Pu
- (2010) Stefan Grimme et al. JOURNAL OF CHEMICAL PHYSICS
- Graphite-diamond phase coexistence study employing a neural-network mapping of theab initiopotential energy surface
- (2010) Rustam Z. Khaliullin 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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