Machine learning scheme for fast extraction of chemically interpretable interatomic potentials

A fingerprint based metric for measuring similarities of crystalline structures

(2016) Li Zhu et al.   JOURNAL OF CHEMICAL PHYSICS

Crystal structure representations for machine learning models of formation energies

(2015) Felix Faber et al.   INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY

Big Data Meets Quantum Chemistry Approximations: The Δ-Machine Learning Approach

(2015) Raghunathan Ramakrishnan et al.   Journal of Chemical Theory and Computation

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

Adaptive machine learning framework to accelerateab initiomolecular dynamics

(2014) Venkatesh Botu et al.   INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY

Accuracy and transferability of Gaussian approximation potential models for tungsten

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

A Density-Functional Theory-Based Neural Network Potential for Water Clusters Including van der Waals Corrections

(2013) Tobias Morawietz et al.   JOURNAL OF PHYSICAL CHEMISTRY A

Machine-learning approach for one- and two-body corrections to density functional theory: Applications to molecular and condensed water

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

On representing chemical environments

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

Unexpected Stable Stoichiometries of Sodium Chlorides

(2013) W. Zhang et al.   SCIENCE

New developments in evolutionary structure prediction algorithm USPEX

(2012) Andriy O. Lyakhov et al.   COMPUTER PHYSICS COMMUNICATIONS

Construction of high-dimensional neural network potentials using environment-dependent atom pairs

(2012) K. V. Jovan Jose et al.   JOURNAL OF CHEMICAL PHYSICS

Microscopic Origins of the Anomalous Melting Behavior of Sodium under High Pressure

(2012) Hagai Eshet et al.   PHYSICAL REVIEW LETTERS

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

(2011) Jörg Behler   PHYSICAL CHEMISTRY CHEMICAL PHYSICS

High-dimensional neural-network potentials for multicomponent systems: Applications to zinc oxide

(2011) Nongnuch Artrith et al.   PHYSICAL REVIEW B

Van der Waals density functionals applied to solids

(2011) Jiří Klimeš et al.   PHYSICAL REVIEW B

Crystal fingerprint space – a novel paradigm for studying crystal-structure sets

(2010) Mario Valle et al.   ACTA CRYSTALLOGRAPHICA SECTION A

Ab initioquality neural-network potential for sodium

(2010) Hagai Eshet 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

How to quantify energy landscapes of solids

(2009) Artem R. Oganov et al.   JOURNAL OF CHEMICAL PHYSICS

Ionic high-pressure form of elemental boron

(2009) Artem R. Oganov et al.   NATURE

Transparent dense sodium

(2009) Yanming Ma et al.   NATURE

Metadynamics Simulations of the High-Pressure Phases of Silicon Employing a High-Dimensional Neural Network Potential

(2008) Jörg Behler et al.   PHYSICAL REVIEW LETTERS