Molecular force fields with gradient-domain machine learning: Construction and application to dynamics of small molecules with coupled cluster forces
出版年份 2019 全文链接
标题
Molecular force fields with gradient-domain machine learning: Construction and application to dynamics of small molecules with coupled cluster forces
作者
关键词
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出版物
JOURNAL OF CHEMICAL PHYSICS
Volume 150, Issue 11, Pages 114102
出版商
AIP Publishing
发表日期
2019-03-19
DOI
10.1063/1.5078687
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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。- Convolutional neural networks for atomistic systems
- (2018) Kevin Ryczko et al. COMPUTATIONAL MATERIALS SCIENCE
- Non-covalent interactions across organic and biological subsets of chemical space: Physics-based potentials parametrized from machine learning
- (2018) Tristan Bereau et al. JOURNAL OF CHEMICAL PHYSICS
- An atomistic fingerprint algorithm for learning ab initio molecular force fields
- (2018) Yu-Hang Tang et al. JOURNAL OF CHEMICAL PHYSICS
- Hierarchical modeling of molecular energies using a deep neural network
- (2018) Nicholas Lubbers 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
- Predicting molecular properties with covariant compositional networks
- (2018) Truong Son Hy et al. JOURNAL OF CHEMICAL PHYSICS
- Solid harmonic wavelet scattering for predictions of molecule properties
- (2018) Michael Eickenberg et al. JOURNAL OF CHEMICAL PHYSICS
- Psi4NumPy: An Interactive Quantum Chemistry Programming Environment for Reference Implementations and Rapid Development
- (2018) Daniel G. A. Smith et al. Journal of Chemical Theory and Computation
- Many-Body Descriptors for Predicting Molecular Properties with Machine Learning: Analysis of Pairwise and Three-Body Interactions in Molecules
- (2018) Wiktor Pronobis et al. Journal of Chemical Theory and Computation
- 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
- VAMPnets for deep learning of molecular kinetics
- (2018) Andreas Mardt et al. Nature Communications
- Rotational Characterization of an n → π* Interaction in a Pyridine–Formaldehyde Adduct
- (2018) Susana Blanco et al. Journal of Physical Chemistry Letters
- Towards exact molecular dynamics simulations with machine-learned force fields
- (2018) Stefan Chmiela et al. Nature Communications
- SchNetPack: A Deep Learning Toolbox For Atomistic Systems
- (2018) K. T. Schütt et al. Journal of Chemical Theory and Computation
- The many-body expansion combined with neural networks
- (2017) Kun Yao et al. JOURNAL OF CHEMICAL PHYSICS
- Interatomic inelastic current
- (2017) Tim Hansen et al. JOURNAL OF CHEMICAL PHYSICS
- Structure-based sampling and self-correcting machine learning for accurate calculations of potential energy surfaces and vibrational levels
- (2017) Pavlo O. Dral et al. JOURNAL OF CHEMICAL PHYSICS
- A Unified Theory for the Blue- and Red-Shifting Phenomena in Hydrogen and Halogen Bonds
- (2017) Changwei Wang et al. Journal of Chemical Theory and Computation
- Prediction Errors of Molecular Machine Learning Models Lower than Hybrid DFT Error
- (2017) Felix A. Faber et al. Journal of Chemical Theory and Computation
- Psi4 1.1: An Open-Source Electronic Structure Program Emphasizing Automation, Advanced Libraries, and Interoperability
- (2017) Robert M. Parrish et al. Journal of Chemical Theory and Computation
- Franck Condon spectra of the 2-tolunitrile dimer and the binary 2-tolunitrile water cluster in the gas phase
- (2017) Felix Gmerek et al. JOURNAL OF MOLECULAR STRUCTURE
- Special Issue: Intramolecular Hydrogen Bonding 2017
- (2017) Steve Scheiner MOLECULES
- Probing the origins of vibrational mode specificity in intramolecular dynamics through picosecond time-resolved photoelectron imaging studies
- (2017) Julia A. Davies et al. PHYSICAL CHEMISTRY CHEMICAL PHYSICS
- 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
- Influences of lone-pair electrons on directionality of hydrogen bonds formed by hydrophilic amino acid side chains in molecular dynamics simulation
- (2017) Tomotaka Oroguchi et al. Scientific Reports
- 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
- Unconventional N-H…N Hydrogen Bonds Involving Proline Backbone Nitrogen in Protein Structures
- (2016) R. N. V. Krishna Deepak et al. BIOPHYSICAL JOURNAL
- Electronic spectra of 2- and 3-tolunitrile in the gas phase. I. A study of methyl group internal rotation via rovibronically resolved spectroscopy
- (2016) José Arturo Ruiz-Santoyo et al. JOURNAL OF CHEMICAL PHYSICS
- 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
- Gaussian approximation potentials: A brief tutorial introduction
- (2015) Albert P. Bartók et al. INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY
- Big Data Meets Quantum Chemistry Approximations: The Δ-Machine Learning Approach
- (2015) Raghunathan Ramakrishnan et al. Journal of Chemical Theory and Computation
- 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
- 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
- Probing the structures of neutral boron clusters using infrared/vacuum ultraviolet two color ionization: B11, B16, and B17
- (2012) Constantin Romanescu et al. JOURNAL OF CHEMICAL PHYSICS
- Construction of high-dimensional neural network potentials using environment-dependent atom pairs
- (2012) K. V. Jovan Jose et al. JOURNAL OF CHEMICAL PHYSICS
- Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning
- (2012) Matthias Rupp et al. PHYSICAL REVIEW LETTERS
- Peculiarities of quasi-aromatic hydrogen bonding
- (2012) Agata Martyniak et al. RSC Advances
- Atom-centered symmetry functions for constructing high-dimensional neural network potentials
- (2011) Jörg Behler JOURNAL OF CHEMICAL PHYSICS
- Ann→π* Interaction in Aspirin: Implications for Structure and Reactivity
- (2011) Amit Choudhary et al. JOURNAL OF ORGANIC CHEMISTRY
- Neural network potential-energy surfaces in chemistry: a tool for large-scale simulations
- (2011) Jörg Behler PHYSICAL CHEMISTRY CHEMICAL PHYSICS
- Psi4: an open-source ab initio electronic structure program
- (2011) Justin M. Turney et al. Wiley Interdisciplinary Reviews-Computational Molecular Science
- Intramolecular Hydrogen Bonding in Medicinal Chemistry
- (2010) Bernd Kuhn et al. JOURNAL OF MEDICINAL CHEMISTRY
- The identification of the two missing conformers of gas-phase alanine: a jet-cooled Raman spectroscopy study
- (2010) Roman M. Balabin PHYSICAL CHEMISTRY CHEMICAL PHYSICS
- Gaussian Approximation Potentials: The Accuracy of Quantum Mechanics, without the Electrons
- (2010) Albert P. Bartók et al. PHYSICAL REVIEW LETTERS
- Ab initio molecular simulations with numeric atom-centered orbitals
- (2009) Volker Blum et al. COMPUTER PHYSICS COMMUNICATIONS
- On the Intramolecular Origin of the Blue Shift of A−H Stretching Frequencies: Triatomic Hydrides HAX
- (2009) Alfred Karpfen et al. JOURNAL OF PHYSICAL CHEMISTRY A
- Accurate Molecular Van Der Waals Interactions from Ground-State Electron Density and Free-Atom Reference Data
- (2009) Alexandre Tkatchenko et al. PHYSICAL REVIEW LETTERS
- Structures of Neutral Au7, Au19, and Au20 Clusters in the Gas Phase
- (2008) P. Gruene et al. SCIENCE
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