Related references
Note: Only part of the references are listed.Fast and Accurate Uncertainty Estimation in Chemical Machine Learning
Felix Musil et al.
JOURNAL OF CHEMICAL THEORY AND COMPUTATION (2019)
DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics
Han Wang et al.
COMPUTER PHYSICS COMMUNICATIONS (2018)
Metadynamics for training neural network model chemistries: A competitive assessment
John E. Herr et al.
JOURNAL OF CHEMICAL PHYSICS (2018)
Less is more: Sampling chemical space with active learning
Justin S. Smith et al.
JOURNAL OF CHEMICAL PHYSICS (2018)
Reinforced dynamics for enhanced sampling in large atomic and molecular systems
Linfeng Zhang et al.
JOURNAL OF CHEMICAL PHYSICS (2018)
DeePCG: Constructing coarse-grained models via deep neural networks
Linfeng Zhang et al.
JOURNAL OF CHEMICAL PHYSICS (2018)
Atomic Energies from a Convolutional Neural Network
Xin Chen et al.
JOURNAL OF CHEMICAL THEORY AND COMPUTATION (2018)
Deep Potential Molecular Dynamics: A Scalable Model with the Accuracy of Quantum Mechanics
Linfeng Zhang et al.
PHYSICAL REVIEW LETTERS (2018)
Silicon Liquid Structure and Crystal Nucleation from Ab Initio Deep Metadynamics
Luigi Bonati et al.
PHYSICAL REVIEW LETTERS (2018)
Machine Learning a General-Purpose Interatomic Potential for Silicon
Albert P. Bartok et al.
PHYSICAL REVIEW X (2018)
Deep Potential: A General Representation of a Many-Body Potential Energy Surface
Jiequn Han et al.
COMMUNICATIONS IN COMPUTATIONAL PHYSICS (2018)
Machine Learning Force Fields: Construction, Validation, and Outlook
V. Botu et al.
JOURNAL OF PHYSICAL CHEMISTRY C (2017)
ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost
J. S. Smith et al.
CHEMICAL SCIENCE (2017)
ANI-1, A data set of 20 million calculated off-equilibrium conformations for organic molecules
Justin S. Smith et al.
SCIENTIFIC DATA (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)
First-principles investigation of strain effects on the stacking fault energies, dislocation core structure, and Peierls stress of magnesium and its alloys
S. H. Zhang et al.
PHYSICAL REVIEW B (2017)
Stratified construction of neural network based interatomic models for multicomponent materials
Samad Hajinazar et al.
PHYSICAL REVIEW B (2017)
Impurity effect of Mg on the generalized planar fault energy of Al
Dongdong Zhao et al.
JOURNAL OF MATERIALS SCIENCE (2016)
Surface energies of elemental crystals
Richard Tran et al.
SCIENTIFIC DATA (2016)
Python Materials Genomics (pymatgen): A robust, open-source python library for materials analysis
Shyue Ping Ong et al.
COMPUTATIONAL MATERIALS SCIENCE (2013)
Machine learning of molecular electronic properties in chemical compound space
Gregoire Montavon et al.
NEW JOURNAL OF PHYSICS (2013)
Commentary: The Materials Project: A materials genome approach to accelerating materials innovation
Anubhav Jain et al.
APL MATERIALS (2013)
Diffusion quantum Monte Carlo study of the equation of state and point defects in aluminum
Randolph Q. Hood et al.
PHYSICAL REVIEW B (2012)
Modified embedded atom method potential for Al, Si, Mg, Cu, and Fe alloys
B. Jelinek et al.
PHYSICAL REVIEW B (2012)
Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning
Matthias Rupp et al.
PHYSICAL REVIEW LETTERS (2012)
Gaussian Approximation Potentials: The Accuracy of Quantum Mechanics, without the Electrons
Albert P. Bartok et al.
PHYSICAL REVIEW LETTERS (2010)
Melting curve of aluminum up to 300 GPa obtained through ab initio molecular dynamics simulations
J. Bouchet et al.
PHYSICAL REVIEW B (2009)
Generalized neural-network representation of high-dimensional potential-energy surfaces
Joerg Behler et al.
PHYSICAL REVIEW LETTERS (2007)
Melting of aluminum, molybdenum, and the light actinides
M Ross et al.
PHYSICAL REVIEW B (2004)
ReaxFF: A reactive force field for hydrocarbons
ACT van Duin et al.
JOURNAL OF PHYSICAL CHEMISTRY A (2001)
Share Paper
