Constrained crystals deep convolutional generative adversarial network for the inverse design of crystal structures

Inverse design of porous materials using artificial neural networks

(2020) Baekjun Kim et al.   Science Advances

Generative Adversarial Networks for Crystal Structure Prediction

(2020) Sungwon Kim et al.   ACS Central Science

3-D Inorganic Crystal Structure Generation and Property Prediction via Representation Learning

(2020) Callum J. Court et al.   Journal of Chemical Information and Modeling

New frontiers for the materials genome initiative

(2019) Juan J. de Pablo et al.   npj Computational Materials

Machine Learning Interatomic Potentials as Emerging Tools for Materials Science

(2019) Volker L. Deringer et al.   ADVANCED MATERIALS

Recent advances and applications of machine learning in solid-state materials science

(2019) Jonathan Schmidt et al.   npj Computational Materials

Crystal Structure Prediction via Deep Learning

(2018) Kevin Ryan et al.   JOURNAL OF THE AMERICAN CHEMICAL SOCIETY

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

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

Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties

(2018) Tian Xie et al.   PHYSICAL REVIEW LETTERS

Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules

(2018) Rafael Gómez-Bombarelli et al.   ACS Central Science

Crystal structure prediction accelerated by Bayesian optimization

(2018) Tomoki Yamashita et al.   PHYSICAL REVIEW MATERIALS

High Throughput Screening of Magnetic Antiperovskites

(2018) Harish K. Singh et al.   CHEMISTRY OF MATERIALS

Inverse molecular design using machine learning: Generative models for matter engineering

(2018) Benjamin Sanchez-Lengeling et al.   SCIENCE

Comparing molecules and solids across structural and alchemical space

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

Learning from the Harvard Clean Energy Project: The Use of Neural Networks to Accelerate Materials Discovery

(2015) Edward O. Pyzer-Knapp et al.   ADVANCED FUNCTIONAL MATERIALS

Deep learning

(2015) Yann LeCun et al.   NATURE

The Materials Genome Initiative, the interplay of experiment, theory and computation

(2014) Juan J. de Pablo et al.   CURRENT OPINION IN SOLID STATE & MATERIALS SCIENCE

Materials Design and Discovery with High-Throughput Density Functional Theory: The Open Quantum Materials Database (OQMD)

(2013) James E. Saal et al.   JOM

Global Structural Optimization of Tungsten Borides

(2013) Quan Li et al.   PHYSICAL REVIEW LETTERS

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

CALYPSO: A method for crystal structure prediction

(2012) Yanchao Wang et al.   COMPUTER PHYSICS COMMUNICATIONS

High throughput density functional investigations of the stability, electronic structure and thermoelectric properties of binary silicides

(2012) Ingo Opahle et al.   PHYSICAL CHEMISTRY CHEMICAL PHYSICS

Ab initiorandom structure searching

(2011) Chris J Pickard et al.   JOURNAL OF PHYSICS-CONDENSED MATTER

Evolutionary search for superhard materials: Methodology and applications to forms of carbon and TiO2

(2011) Andriy O. Lyakhov et al.   PHYSICAL REVIEW B

The Fourth Paradigm: Data-Intensive Scientific Discovery [Point of View]

(2011) Kristin M. Tolle et al.   PROCEEDINGS OF THE IEEE

Topological insulators in Bi2Se3, Bi2Te3 and Sb2Te3 with a single Dirac cone on the surface

(2009) Haijun Zhang et al.   Nature Physics