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。Accurate molecular polarizabilities with coupled cluster theory and machine learning
David M. Wilkins et al.
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA (2019)
Statistical Aspects of Wasserstein Distances
Victor M. Panaretos et al.
ANNUAL REVIEW OF STATISTICS AND ITS APPLICATION, VOL 6 (2019)
Transferable Machine-Learning Model of the Electron Density
Andrea Grisafi et al.
ACS CENTRAL SCIENCE (2019)
Atomic cluster expansion for accurate and transferable interatomic potentials
Ralf Drautz
PHYSICAL REVIEW B (2019)
Alchemical and structural distribution based representation for universal quantum machine learning
Felix A. Faber et al.
JOURNAL OF CHEMICAL PHYSICS (2018)
wACSF-Weighted atom-centered symmetry functions as descriptors in machine learning potentials
M. Gastegger et al.
JOURNAL OF CHEMICAL PHYSICS (2018)
Automatic selection of atomic fingerprints and reference configurations for machine-learning potentials
Giulio Imbalzano et al.
JOURNAL OF CHEMICAL PHYSICS (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)
Insightful classification of crystal structures using deep learning
Angelo Ziletti et al.
NATURE COMMUNICATIONS (2018)
Chemical shifts in molecular solids by machine learning
Federico M. Paruzzo et al.
NATURE COMMUNICATIONS (2018)
Feature optimization for atomistic machine learning yields a data-driven construction of the periodic table of the elements
Michael J. Willatt et al.
PHYSICAL CHEMISTRY CHEMICAL PHYSICS (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)
Predicting Catalytic Activity of Nanoparticles by a DFT-Aided Machine-Learning Algorithm
Ryosuke Jinnouchi et al.
JOURNAL OF PHYSICAL CHEMISTRY LETTERS (2017)
WAVELET SCATTERING REGRESSION OF QUANTUM CHEMICAL ENERGIES
Matthew Hirn et al.
MULTISCALE MODELING & SIMULATION (2017)
Machine learning molecular dynamics for the simulation of infrared spectra
Michael Gastegger et al.
CHEMICAL SCIENCE (2017)
ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost
J. S. Smith et al.
CHEMICAL SCIENCE (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)
Neural network potential for Al-Mg-Si alloys (vol 1, 053604, 2017)
Ryo Kobayashi et al.
PHYSICAL REVIEW MATERIALS (2017)
A Universal 3D Voxel Descriptor for Solid-State Material Informatics with Deep Convolutional Neural Networks
Seiji Kajita et al.
SCIENTIFIC REPORTS (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)
Machine learning based interatomic potential for amorphous carbon
Volker L. Deringer et al.
PHYSICAL REVIEW B (2017)
Amp: A modular approach to machine learning in atomistic simulations
Alireza Khorshidi et al.
COMPUTER PHYSICS COMMUNICATIONS (2016)
Communication: Understanding molecular representations in machine learning: The role of uniqueness and target similarity
Bing Huang et al.
JOURNAL OF CHEMICAL PHYSICS (2016)
MOMENT TENSOR POTENTIALS: A CLASS OF SYSTEMATICALLY IMPROVABLE INTERATOMIC POTENTIALS
Alexander V. Shapeev
MULTISCALE MODELING & SIMULATION (2016)
Comparing molecules and solids across structural and alchemical space
Sandip De et al.
PHYSICAL CHEMISTRY CHEMICAL PHYSICS (2016)
Machine Learning Energies of 2 Million Elpasolite (ABC2D6) Crystals
Felix A. Faber et al.
PHYSICAL REVIEW LETTERS (2016)
A general-purpose machine learning framework for predicting properties of inorganic materials
Logan Ward et al.
NPJ COMPUTATIONAL MATERIALS (2016)
Many Molecular Properties from One Kernel in Chemical Space
Raghunathan Ramakrishnan et al.
CHIMIA (2015)
Fourier series of atomic radial distribution functions: A molecular fingerprint for machine learning models of quantum chemical properties
O. Anatole von Lilienfeld et al.
INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY (2015)
Systematic comparison of crystalline and amorphous phases: Charting the landscape of water structures and transformations
Fabio Pietrucci 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)
Permutation invariant polynomial neural network approach to fitting potential energy surfaces. III. Molecule-surface interactions
Bin Jiang et al.
JOURNAL OF CHEMICAL PHYSICS (2014)
How to represent crystal structures for machine learning: Towards fast prediction of electronic properties
K. T. Schuett et al.
PHYSICAL REVIEW B (2014)
Accuracy and transferability of Gaussian approximation potential models for tungsten
Wojciech J. Szlachta et al.
PHYSICAL REVIEW B (2014)
Machine learning methods in chemoinformatics
John B. O. Mitchell
WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL MOLECULAR SCIENCE (2014)
Finding Unprecedentedly Low-Thermal-Conductivity Half-Heusler Semiconductors via High-Throughput Materials Modeling
Jesus Carrete et al.
PHYSICAL REVIEW X (2014)
Metrics for measuring distances in configuration spaces
Ali Sadeghi et al.
JOURNAL OF CHEMICAL PHYSICS (2013)
Demonstrating the Transferability and the Descriptive Power of Sketch-Map
Michele Ceriotti 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 (vol 87, 184115, 2013)
Albert P. Bartok et al.
PHYSICAL REVIEW B (2013)
Machine-learning approach for one- and two-body corrections to density functional theory: Applications to molecular and condensed water
Albert P. Bartok et al.
PHYSICAL REVIEW B (2013)
A neural network potential-energy surface for the water dimer based on environment-dependent atomic energies and charges
Tobias Morawietz 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)
O(N) methods in electronic structure calculations
D. R. Bowler et al.
REPORTS ON PROGRESS IN PHYSICS (2012)
Permutationally Invariant Polynomial Basis for Molecular Energy Surface Fitting via Monomial Symmetrization
Zhen Xie et al.
JOURNAL OF CHEMICAL THEORY AND COMPUTATION (2010)
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)
CUR matrix decompositions for improved data analysis
Michael W. Mahoney et al.
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA (2009)
Generalized neural-network representation of high-dimensional potential-energy surfaces
Joerg Behler et al.
PHYSICAL REVIEW LETTERS (2007)
Nearsightedness of electronic matter
E Prodan et al.
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA (2005)