Related references
Note: Only part of the references are listed.Convolutional neural networks for atomistic systems
Kevin Ryczko et al.
COMPUTATIONAL MATERIALS SCIENCE (2018)
Non-covalent interactions across organic and biological subsets of chemical space: Physics-based potentials parametrized from machine learning
Tristan Bereau et al.
JOURNAL OF CHEMICAL PHYSICS (2018)
High-dimensional neural network potentials for solvation: The case of protonated water clusters in helium
Christoph Schran et al.
JOURNAL OF CHEMICAL PHYSICS (2018)
An atomistic fingerprint algorithm for learning ab initio molecular force fields
Yu-Hang Tang et al.
JOURNAL OF CHEMICAL PHYSICS (2018)
Hierarchical modeling of molecular energies using a deep neural network
Nicholas Lubbers et al.
JOURNAL OF CHEMICAL PHYSICS (2018)
Predicting molecular properties with covariant compositional networks
Truong Son Hy et al.
JOURNAL OF CHEMICAL PHYSICS (2018)
Solid harmonic wavelet scattering for predictions of molecule properties
Michael Eickenberg et al.
JOURNAL OF CHEMICAL PHYSICS (2018)
Many-Body Descriptors for Predicting Molecular Properties with Machine Learning: Analysis of Pairwise and Three-Body Interactions in Molecules
Wiktor Pronobis et al.
JOURNAL OF CHEMICAL THEORY AND COMPUTATION (2018)
Symmetry-Adapted Machine Learning for Tensorial Properties of Atomistic Systems
Andrea Grisafi et al.
PHYSICAL REVIEW LETTERS (2018)
Deep Potential Molecular Dynamics: A Scalable Model with the Accuracy of Quantum Mechanics
Linfeng Zhang et al.
PHYSICAL REVIEW LETTERS (2018)
The TensorMol-0.1 model chemistry: a neural network augmented with long-range physics
Kun Yao et al.
CHEMICAL SCIENCE (2018)
VAMPnets for deep learning of molecular kinetics
Andreas Mardt et al.
NATURE COMMUNICATIONS (2018)
Exploring a potential energy surface by machine learning for characterizing atomic transport
Kenta Kanamori et al.
PHYSICAL REVIEW B (2018)
Efficient nonparametric n-body force fields from machine learning
Aldo Glielmo et al.
PHYSICAL REVIEW B (2018)
The many-body expansion combined with neural networks
Kun Yao et al.
JOURNAL OF CHEMICAL PHYSICS (2017)
Learning molecular energies using localized graph kernels
Gregoire Ferre et al.
JOURNAL OF CHEMICAL PHYSICS (2017)
Structure-based sampling and self-correcting machine learning for accurate calculations of potential energy surfaces and vibrational levels
Pavlo O. Dral et al.
JOURNAL OF CHEMICAL PHYSICS (2017)
Prediction Errors of Molecular Machine Learning Models Lower than Hybrid DFT Error
Felix A. Faber et al.
JOURNAL OF CHEMICAL THEORY AND COMPUTATION (2017)
PSI4 1.1: An Open-Source Electronic Structure Program Emphasizing Automation, Advanced Libraries, and Interoperability
Robert M. Parrish et al.
JOURNAL OF CHEMICAL THEORY AND COMPUTATION (2017)
Many-Body Coarse-Grained Interactions Using Gaussian Approximation Potentials
S. T. John et al.
JOURNAL OF PHYSICAL CHEMISTRY B (2017)
Use machine learning to find energy materials
Phil De Luna et al.
NATURE (2017)
Machine learning molecular dynamics for the simulation of infrared spectra
Michael Gastegger et al.
CHEMICAL SCIENCE (2017)
Bypassing the Kohn-Sham equations with machine learning
Felix Brockherde et al.
NATURE COMMUNICATIONS (2017)
Machine learning unifies the modeling of materials and molecules
Albert P. Bartok et al.
SCIENCE ADVANCES (2017)
Machine learning of accurate energy-conserving molecular force fields
Stefan Chmiela et al.
SCIENCE ADVANCES (2017)
Active learning of linearly parametrized interatomic potentials
Evgeny V. Podryabinkin et al.
COMPUTATIONAL MATERIALS SCIENCE (2017)
Efficient and accurate machine-learning interpolation of atomic energies in compositions with many species
Nongnuch Artrith et al.
PHYSICAL REVIEW B (2017)
Accurate interatomic force fields via machine learning with covariant kernels
Aldo Glielmo et al.
PHYSICAL REVIEW B (2017)
Perspective: Machine learning potentials for atomistic simulations
Joerg Behler
JOURNAL OF CHEMICAL PHYSICS (2016)
Comparing molecules and solids across structural and alchemical space
Sandip De et al.
PHYSICAL CHEMISTRY CHEMICAL PHYSICS (2016)
On invariance and selectivity in representation learning
Fabio Anselmi et al.
INFORMATION AND INFERENCE-A JOURNAL OF THE IMA (2016)
Gaussian approximation potentials: A brief tutorial introduction
Albert P. Bartok et al.
INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY (2015)
Machine Learning for Quantum Mechanical Properties of Atoms in Molecules
Matthias Rupp et al.
JOURNAL OF PHYSICAL CHEMISTRY LETTERS (2015)
Machine Learning Predictions of Molecular Properties: Accurate Many-Body Potentials and Nonlocality in Chemical Space
Katja Hansen et al.
JOURNAL OF PHYSICAL CHEMISTRY LETTERS (2015)
Learning scheme to predict atomic forces and accelerate materials simulations
V. Botu et al.
PHYSICAL REVIEW B (2015)
Molecular Dynamics with On-the-Fly Machine Learning of Quantum-Mechanical Forces
Zhenwei Li et al.
PHYSICAL REVIEW LETTERS (2015)
i-PI: A Python interface for ab initio path integral molecular dynamics simulations
Michele Ceriotti et al.
COMPUTER PHYSICS COMMUNICATIONS (2014)
A compact and accurate semi-global potential energy surface for malonaldehyde from constrained least squares regression
Wataru Mizukami et al.
JOURNAL OF CHEMICAL PHYSICS (2014)
Assessment and Validation of Machine Learning Methods for Predicting Molecular Atomization Energies
Katja Hansen et al.
JOURNAL OF CHEMICAL THEORY AND COMPUTATION (2013)
Machine learning of molecular electronic properties in chemical compound space
Gregoire Montavon et al.
NEW JOURNAL OF PHYSICS (2013)
On representing chemical environments
Albert P. Bartok et al.
PHYSICAL REVIEW B (2013)
Construction of high-dimensional neural network potentials using environment-dependent atom pairs
K. V. Jovan Jose et al.
JOURNAL OF CHEMICAL PHYSICS (2012)
Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning
Matthias Rupp et al.
PHYSICAL REVIEW LETTERS (2012)
Ethanol Monomers and Dimers Revisited: A Raman Study of Conformational Preferences and Argon Nanocoating Effects
Tobias N. Wassermann et al.
JOURNAL OF PHYSICAL CHEMISTRY A (2010)
Gaussian Approximation Potentials: The Accuracy of Quantum Mechanics, without the Electrons
Albert P. Bartok et al.
PHYSICAL REVIEW LETTERS (2010)
Ab initio molecular simulations with numeric atom-centered orbitals
Volker Blum et al.
COMPUTER PHYSICS COMMUNICATIONS (2009)
Accurate Molecular Van Der Waals Interactions from Ground-State Electron Density and Free-Atom Reference Data
Alexandre Tkatchenko et al.
PHYSICAL REVIEW LETTERS (2009)
Generalized neural-network representation of high-dimensional potential-energy surfaces
Joerg Behler et al.
PHYSICAL REVIEW LETTERS (2007)
Share Paper
