相关参考文献
注意：仅列出部分参考文献，下载原文获取全部文献信息。Quantum-chemical insights from deep tensor neural networks
Kristof T. Schuett et al.
NATURE COMMUNICATIONS (2017)
MOMENT TENSOR POTENTIALS: A CLASS OF SYSTEMATICALLY IMPROVABLE INTERATOMIC POTENTIALS
Alexander V. Shapeev
MULTISCALE MODELING & SIMULATION (2016)
Constructing high-dimensional neural network potentials: A tutorial review
Joerg Behler
INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY (2015)
Gaussian approximation potentials: A brief tutorial introduction
Albert P. Bartok et al.
INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY (2015)
Permutation-invariant distance between atomic configurations
Gregoire Ferre et al.
JOURNAL OF CHEMICAL PHYSICS (2015)
Spectral neighbor analysis method for automated generation of quantum-accurate interatomic potentials
A. P. Thompson et al.
JOURNAL OF COMPUTATIONAL PHYSICS (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)
Molecular Dynamics with On-the-Fly Machine Learning of Quantum-Mechanical Forces
Zhenwei Li et al.
PHYSICAL REVIEW LETTERS (2015)
Next generation interatomic potentials for condensed systems
Christopher Michael Handley et al.
EUROPEAN PHYSICAL JOURNAL B (2014)
Representing potential energy surfaces by high-dimensional neural network potentials
J. Behler
JOURNAL OF PHYSICS-CONDENSED MATTER (2014)
How to represent crystal structures for machine learning: Towards fast prediction of electronic properties
K. T. Schuett et al.
PHYSICAL REVIEW B (2014)
Assessment and Validation of Machine Learning Methods for Predicting Molecular Atomization Energies
Katja Hansen et al.
JOURNAL OF CHEMICAL THEORY AND COMPUTATION (2013)
On representing chemical environments
Albert P. Bartok et al.
PHYSICAL REVIEW B (2013)
Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning
Matthias Rupp et al.
PHYSICAL REVIEW LETTERS (2012)
Two new graphs kernels in chemoinformatics
Benoit Gauezere et al.
PATTERN RECOGNITION LETTERS (2012)
Wavelets on graphs via spectral graph theory
David K. Hammond et al.
APPLIED AND COMPUTATIONAL HARMONIC ANALYSIS (2011)
Atom-centered symmetry functions for constructing high-dimensional neural network potentials
Joerg Behler
JOURNAL OF CHEMICAL PHYSICS (2011)
Gaussian Approximation Potentials: The Accuracy of Quantum Mechanics, without the Electrons
Albert P. Bartok et al.
PHYSICAL REVIEW LETTERS (2010)
Permutationally invariant potential energy surfaces in high dimensionality
Bastiaan J. Braams et al.
INTERNATIONAL REVIEWS IN PHYSICAL CHEMISTRY (2009)
GRAPH WAVELET ALIGNMENT KERNELS FOR DRUG VIRTUAL SCREENING
AARON SMALTER et al.
Journal of Bioinformatics and Computational Biology (2009)
Kernel methods in machine learning
Thomas Hofmann et al.
ANNALS OF STATISTICS (2008)
Generalized neural-network representation of high-dimensional potential-energy surfaces
Joerg Behler et al.
PHYSICAL REVIEW LETTERS (2007)
Modeling cellular machinery through biological network comparison
R Sharan et al.
NATURE BIOTECHNOLOGY (2006)
Graph kernels for chemical informatics
L Ralaivola et al.
NEURAL NETWORKS (2005)
Multiple surface long-range interaction potentials between C (P-3(j)) and closed-shell molecules
RPA Bettens et al.
JOURNAL OF CHEMICAL PHYSICS (2002)
分享论文
