Representations in neural network based empirical potentials

Holistic computational structure screening of more than 12 000 candidates for solid lithium-ion conductor materials

(2017) Austin D. Sendek et al.   Energy & Environmental Science

Quantum-chemical insights from deep tensor neural networks

(2017) Kristof T. Schütt et al.   Nature Communications

Atomic Insight into the Lithium Storage and Diffusion Mechanism of SiO2/Al2O3 Electrodes of Lithium Ion Batteries: ReaxFF Reactive Force Field Modeling

(2016) Alireza Ostadhossein et al.   JOURNAL OF PHYSICAL CHEMISTRY A

Structural Properties of Defects in Glassy Liquids

(2016) Ekin D. Cubuk et al.   JOURNAL OF PHYSICAL CHEMISTRY B

A structural approach to relaxation in glassy liquids

(2016) S. S. Schoenholz et al.   Nature Physics

Relationship between local structure and relaxation in out-of-equilibrium glassy systems

(2016) Samuel S. Schoenholz et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

A general-purpose machine learning framework for predicting properties of inorganic materials

(2016) Logan Ward et al.   npj Computational Materials

Understanding machine-learned density functionals

(2015) Li Li et al.   INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY

Understanding kernel ridge regression: Common behaviors from simple functions to density functionals

(2015) Kevin Vu et al.   INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY

Long-Time Dynamics through Parallel Trajectory Splicing

(2015) Danny Perez et al.   Journal of Chemical Theory and Computation

Representing the potential-energy surface of protonated water clusters by high-dimensional neural network potentials

(2015) Suresh Kondati Natarajan et al.   PHYSICAL CHEMISTRY CHEMICAL PHYSICS

Identifying Structural Flow Defects in Disordered Solids Using Machine-Learning Methods

(2015) E. D. Cubuk et al.   PHYSICAL REVIEW LETTERS

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

(2015) Zhenwei Li et al.   PHYSICAL REVIEW LETTERS

Big Data of Materials Science: Critical Role of the Descriptor

(2015) Luca M. Ghiringhelli et al.   PHYSICAL REVIEW LETTERS

Theory of Structural Transformation in Lithiated Amorphous Silicon

(2014) Ekin D. Cubuk et al.   NANO LETTERS

Understanding the Composition and Activity of Electrocatalytic Nanoalloys in Aqueous Solvents: A Combination of DFT and Accurate Neural Network Potentials

(2014) Nongnuch Artrith et al.   NANO LETTERS

Sparse representation for a potential energy surface

(2014) Atsuto Seko et al.   PHYSICAL REVIEW B

Representation Learning: A Review and New Perspectives

(2013) Y. Bengio et al.   IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE

Demonstrating the Transferability and the Descriptive Power of Sketch-Map

(2013) Michele Ceriotti et al.   Journal of Chemical Theory and Computation

Morphological Evolution of Si Nanowires upon Lithiation: A First-Principles Multiscale Model

(2013) Ekin D. Cubuk et al.   NANO LETTERS

The high-throughput highway to computational materials design

(2013) Stefano Curtarolo et al.   NATURE MATERIALS

Machine learning of molecular electronic properties in chemical compound space

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

Accelerated Materials Design of Lithium Superionic Conductors Based on First-Principles Calculations and Machine Learning Algorithms

(2013) Koji Fujimura et al.   Advanced Energy Materials

Decoding the energy landscape: extracting structure, dynamics and thermodynamics

(2012) D. J. Wales   PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES

Neural network potentials for metals and oxides - First applications to copper clusters at zinc oxide

(2012) Nongnuch Artrith et al.   PHYSICA STATUS SOLIDI B-BASIC SOLID STATE PHYSICS

High-dimensional neural network potentials for metal surfaces: A prototype study for copper

(2012) Nongnuch Artrith et al.   PHYSICAL REVIEW B

Finding Density Functionals with Machine Learning

(2012) John C. Snyder et al.   PHYSICAL REVIEW LETTERS

Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning

(2012) Matthias Rupp et al.   PHYSICAL REVIEW LETTERS

Using sketch-map coordinates to analyze and bias molecular dynamics simulations

(2012) G. A. Tribello et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

Nucleation mechanism for the direct graphite-to-diamond phase transition

(2011) Rustam Z. Khaliullin et al.   NATURE MATERIALS

Simplifying the representation of complex free-energy landscapes using sketch-map

(2011) Michele Ceriotti et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

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

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

Ab-initiosimulations of materials using VASP: Density-functional theory and beyond

(2008) Jürgen Hafner   JOURNAL OF COMPUTATIONAL CHEMISTRY

Ab initiostudy of the optical properties of Si-XII

(2008) Brad D. Malone et al.   PHYSICAL REVIEW B

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

(2008) Jörg Behler et al.   PHYSICAL REVIEW LETTERS