On-the-fly active learning of interpretable Bayesian force fields for atomistic rare events

Fast and Accurate Uncertainty Estimation in Chemical Machine Learning

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

Active learning of uniformly accurate interatomic potentials for materials simulation

(2019) Linfeng Zhang et al.   PHYSICAL REVIEW MATERIALS

Phase Transitions of Hybrid Perovskites Simulated by Machine-Learning Force Fields Trained on the Fly with Bayesian Inference

(2019) Ryosuke Jinnouchi et al.   PHYSICAL REVIEW LETTERS

De novo exploration and self-guided learning of potential-energy surfaces

(2019) Noam Bernstein et al.   npj Computational Materials

Machine learning of molecular properties: Locality and active learning

(2018) Konstantin Gubaev 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

Data-Driven Learning of Total and Local Energies in Elemental Boron

(2018) Volker L. Deringer et al.   PHYSICAL REVIEW LETTERS

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

(2018) Linfeng Zhang et al.   PHYSICAL REVIEW LETTERS

Multiobjective genetic training and uncertainty quantification of reactive force fields

(2018) Ankit Mishra et al.   npj Computational Materials

Accelerating high-throughput searches for new alloys with active learning of interatomic potentials

(2018) Konstantin Gubaev et al.   COMPUTATIONAL MATERIALS SCIENCE

Active learning in Gaussian process interpolation of potential energy surfaces

(2018) Elena Uteva et al.   JOURNAL OF CHEMICAL PHYSICS

Double-slit photoelectron interference in strong-field ionization of the neon dimer

(2018) Maksim Kunitski et al.   Nature Communications

The atomic simulation environment—a Python library for working with atoms

(2017) Ask Hjorth Larsen et al.   JOURNAL OF PHYSICS-CONDENSED MATTER

Machine learning molecular dynamics for the simulation of infrared spectra

(2017) Michael Gastegger et al.   Chemical Science

Machine learning of accurate energy-conserving molecular force fields

(2017) Stefan Chmiela et al.   Science Advances

Machine Learning Force Fields: Construction, Validation, and Outlook

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

Moment Tensor Potentials: A Class of Systematically Improvable Interatomic Potentials

(2016) Alexander V. Shapeev   MULTISCALE MODELING & SIMULATION

Comparing molecules and solids across structural and alchemical space

(2016) Sandip De et al.   PHYSICAL CHEMISTRY CHEMICAL PHYSICS

Gaussian approximation potentials: A brief tutorial introduction

(2015) Albert P. Bartók et al.   INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY

Spectral neighbor analysis method for automated generation of quantum-accurate interatomic potentials

(2015) A.P. Thompson et al.   JOURNAL OF COMPUTATIONAL PHYSICS

Molecular Dynamics with On-the-Fly Machine Learning of Quantum-Mechanical Forces

(2015) Zhenwei Li et al.   PHYSICAL REVIEW LETTERS

Accuracy and transferability of Gaussian approximation potential models for tungsten

(2014) Wojciech J. Szlachta et al.   PHYSICAL REVIEW B

Machine Learning Estimates of Natural Product Conformational Energies

(2014) Matthias Rupp et al.   PLoS Computational Biology

On representing chemical environments

(2013) Albert P. Bartók et al.   PHYSICAL REVIEW B

Atom-centered symmetry functions for constructing high-dimensional neural network potentials

(2011) Jörg Behler   JOURNAL OF CHEMICAL PHYSICS

Neural network potential-energy surfaces in chemistry: a tool for large-scale simulations

(2011) Jörg Behler   PHYSICAL CHEMISTRY CHEMICAL PHYSICS

Highly optimized embedded-atom-method potentials for fourteen fcc metals

(2011) H. W. Sheng et al.   PHYSICAL REVIEW B

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

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

QUANTUM ESPRESSO: a modular and open-source software project for quantum simulations of materials

(2009) Paolo Giannozzi et al.   JOURNAL OF PHYSICS-CONDENSED MATTER