Machine learning reveals orbital interaction in materials

Novel mixture model for the representation of potential energy surfaces

(2016) Tien Lam Pham et al.   JOURNAL OF CHEMICAL PHYSICS

Comparing molecules and solids across structural and alchemical space

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

Materials Cartography: Representing and Mining Materials Space Using Structural and Electronic Fingerprints

(2015) Olexandr Isayev et al.   CHEMISTRY OF MATERIALS

The Materials Application Programming Interface (API): A simple, flexible and efficient API for materials data based on REpresentational State Transfer (REST) principles

(2015) Shyue Ping Ong et al.   COMPUTATIONAL MATERIALS SCIENCE

Machine learning for quantum mechanics in a nutshell

(2015) Matthias Rupp   INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY

Gaussian approximation potentials: A brief tutorial introduction

(2015) Albert P. Bartók et al.   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

Big Data of Materials Science: Critical Role of the Descriptor

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

Data mining for materials design: A computational study of single molecule magnet

(2014) Hieu Chi Dam et al.   JOURNAL OF CHEMICAL PHYSICS

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

Accelerating materials property predictions using machine learning

(2013) Ghanshyam Pilania et al.   Scientific Reports

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

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

(2012) Nongnuch Artrith et al.   PHYSICAL REVIEW B

Data mining for materials: Computational experiments withABcompounds

(2012) Yousef Saad et al.   PHYSICAL REVIEW B

Finding Density Functionals with Machine Learning

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

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

(2012) Hagai Eshet et al.   PHYSICAL REVIEW LETTERS

Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning

(2012) Matthias Rupp et al.   PHYSICAL REVIEW LETTERS

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

(2011) Jörg Behler   JOURNAL OF CHEMICAL PHYSICS

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

(2011) Nongnuch Artrith et al.   PHYSICAL REVIEW B

Finding Nature’s Missing Ternary Oxide Compounds Using Machine Learning and Density Functional Theory

(2010) Geoffroy Hautier et al.   CHEMISTRY OF MATERIALS

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

Identifying Zeolite Frameworks with a Machine Learning Approach

(2009) Shujiang Yang et al.   Journal of Physical Chemistry C

970 Million Druglike Small Molecules for Virtual Screening in the Chemical Universe Database GDB-13

(2009) Lorenz C. Blum et al.   JOURNAL OF THE AMERICAN CHEMICAL SOCIETY