A Universal 3D Voxel Descriptor for Solid-State Material Informatics with Deep Convolutional Neural Networks

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

Wavelet Scattering Regression of Quantum Chemical Energies

(2017) Matthew Hirn et al.   MULTISCALE MODELING & SIMULATION

Discovering charge density functionals and structure-property relationships with PROPhet: A general framework for coupling machine learning and first-principles methods

(2017) Brian Kolb et al.   Scientific Reports

An implementation of artificial neural-network potentials for atomistic materials simulations: Performance for TiO 2

(2016) Nongnuch Artrith et al.   COMPUTATIONAL MATERIALS SCIENCE

New opportunities for materials informatics: Resources and data mining techniques for uncovering hidden relationships

(2016) Anubhav Jain et al.   JOURNAL OF MATERIALS RESEARCH

Materials science with large-scale data and informatics: Unlocking new opportunities

(2016) Joanne Hill et al.   MRS BULLETIN

Deep MRI brain extraction: A 3D convolutional neural network for skull stripping

(2016) Jens Kleesiek et al.   NEUROIMAGE

Comparing molecules and solids across structural and alchemical space

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

Machine Learning Energies of 2 Million Elpasolite(ABC2D6)Crystals

(2016) Felix A. Faber et al.   PHYSICAL REVIEW LETTERS

Fourier series of atomic radial distribution functions: A molecular fingerprint for machine learning models of quantum chemical properties

(2015) O. Anatole von Lilienfeld et al.   INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY

Constructing high-dimensional neural network potentials: A tutorial review

(2015) Jörg Behler   INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY

Crystal structure representations for machine learning models of formation energies

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

Prediction of Low-Thermal-Conductivity Compounds with First-Principles Anharmonic Lattice-Dynamics Calculations and Bayesian Optimization

(2015) Atsuto Seko et al.   PHYSICAL REVIEW LETTERS

Accuracy and transferability of Gaussian approximation potential models for tungsten

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

Assessment and Validation of Machine Learning Methods for Predicting Molecular Atomization Energies

(2013) Katja Hansen et al.   Journal of Chemical Theory and Computation

On representing chemical environments

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

3D Convolutional Neural Networks for Human Action Recognition

(2012) Shuiwang Ji et al.   IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE

Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning

(2012) Matthias Rupp et al.   PHYSICAL REVIEW LETTERS