Active learning of uniformly accurate interatomic potentials for materials simulation

Fast and Accurate Uncertainty Estimation in Chemical Machine Learning

(2019) Félix Musil et al.   Journal of Chemical Theory and Computation

DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics

(2018) Han Wang et al.   COMPUTER PHYSICS COMMUNICATIONS

Metadynamics for training neural network model chemistries: A competitive assessment

(2018) John E. Herr et al.   JOURNAL OF CHEMICAL PHYSICS

Less is more: Sampling chemical space with active learning

(2018) Justin S. Smith et al.   JOURNAL OF CHEMICAL PHYSICS

Reinforced dynamics for enhanced sampling in large atomic and molecular systems

(2018) Linfeng Zhang et al.   JOURNAL OF CHEMICAL PHYSICS

DeePCG: Constructing coarse-grained models via deep neural networks

(2018) Linfeng Zhang et al.   JOURNAL OF CHEMICAL PHYSICS

Atomic Energies from a Convolutional Neural Network

(2018) Xin Chen et al.   Journal of Chemical Theory and Computation

Deep Potential Molecular Dynamics: A Scalable Model with the Accuracy of Quantum Mechanics

(2018) Linfeng Zhang et al.   PHYSICAL REVIEW LETTERS

Silicon Liquid Structure and Crystal Nucleation from Ab Initio Deep Metadynamics

(2018) Luigi Bonati et al.   PHYSICAL REVIEW LETTERS

Machine Learning a General-Purpose Interatomic Potential for Silicon

(2018) Albert P. Bartók et al.   Physical Review X

ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost

(2017) J. S. Smith et al.   Chemical Science

ANI-1, A data set of 20 million calculated off-equilibrium conformations for organic molecules

(2017) Justin S. Smith et al.   Scientific Data

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

Impurity effect of Mg on the generalized planar fault energy of Al

(2016) Dongdong Zhao et al.   JOURNAL OF MATERIALS SCIENCE

Machine Learning Force Fields: Construction, Validation, and Outlook

(2016) V. Botu et al.   Journal of Physical Chemistry C

Surface energies of elemental crystals

(2016) Richard Tran et al.   Scientific Data

Deep learning

(2015) Yann LeCun et al.   NATURE

Machine learning of molecular electronic properties in chemical compound space

(2013) Grégoire Montavon et al.   NEW JOURNAL OF PHYSICS

Commentary: The Materials Project: A materials genome approach to accelerating materials innovation

(2013) Anubhav Jain et al.   APL Materials

Python Materials Genomics (pymatgen): A robust, open-source python library for materials analysis

(2012) Shyue Ping Ong et al.   COMPUTATIONAL MATERIALS SCIENCE

Diffusion quantum Monte Carlo study of the equation of state and point defects in aluminum

(2012) Randolph Q. Hood et al.   PHYSICAL REVIEW B

Modified embedded atom method potential for Al, Si, Mg, Cu, and Fe alloys

(2012) B. Jelinek et al.   PHYSICAL REVIEW B

Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning

(2012) Matthias Rupp et al.   PHYSICAL REVIEW LETTERS

Gaussian Approximation Potentials: The Accuracy of Quantum Mechanics, without the Electrons

(2010) Albert P. Bartók et al.   PHYSICAL REVIEW LETTERS

Melting curve of aluminum up to 300 GPa obtained throughab initiomolecular dynamics simulations

(2009) J. Bouchet et al.   PHYSICAL REVIEW B