Machine learning with force-field-inspired descriptors for materials: Fast screening and mapping energy landscape

Alchemical and structural distribution based representation for universal quantum machine learning

(2018) Felix A. Faber et al.   JOURNAL OF CHEMICAL PHYSICS

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

(2018) Tian Xie et al.   PHYSICAL REVIEW LETTERS

Computational screening of high-performance optoelectronic materials using OptB88vdW and TB-mBJ formalisms

(2018) Kamal Choudhary et al.   Scientific Data

Data Mining for New Two- and One-Dimensional Weakly Bonded Solids and Lattice-Commensurate Heterostructures

(2017) Gowoon Cheon et al.   NANO LETTERS

Machine learning reveals orbital interaction in materials

(2017) Tien Lam Pham et al.   SCIENCE AND TECHNOLOGY OF ADVANCED MATERIALS

Universal fragment descriptors for predicting properties of inorganic crystals

(2017) Olexandr Isayev et al.   Nature Communications

High-throughput Identification and Characterization of Two-dimensional Materials using Density functional theory

(2017) Kamal Choudhary et al.   Scientific Reports

Dynamical properties of AlN nanostructures and heterogeneous interfaces predicted using COMB potentials

(2016) Kamal Choudhary et al.   COMPUTATIONAL MATERIALS SCIENCE

Communication: Understanding molecular representations in machine learning: The role of uniqueness and target similarity

(2016) Bing Huang et al.   JOURNAL OF CHEMICAL PHYSICS

Can artificial intelligence create the next wonder material?

(2016) Nicola Nosengo   NATURE

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

(2016) Logan Ward et al.   npj Computational Materials

Machine learning bandgaps of double perovskites

(2016) G. Pilania et al.   Scientific Reports

A Statistical Learning Framework for Materials Science: Application to Elastic Moduli of k-nary Inorganic Polycrystalline Compounds

(2016) Maarten de Jong et al.   Scientific Reports

Crystal structure representations for machine learning models of formation energies

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

ReaxFF molecular dynamics simulations on lithiated sulfur cathode materials

(2015) Md Mahbubul Islam et al.   PHYSICAL CHEMISTRY CHEMICAL PHYSICS

Charting the complete elastic properties of inorganic crystalline compounds

(2015) Maarten de Jong et al.   Scientific Data

Pseudopotentials periodic table: From H to Pu

(2014) Andrea Dal Corso   COMPUTATIONAL MATERIALS SCIENCE

Structures and Phase Transition of a MoS2 Monolayer

(2014) M. Kan et al.   Journal of Physical Chemistry C

Colloidal Synthesis of 1T-WS2 and 2H-WS2 Nanosheets: Applications for Photocatalytic Hydrogen Evolution

(2014) Benoit Mahler et al.   JOURNAL OF THE AMERICAN CHEMICAL SOCIETY

How to represent crystal structures for machine learning: Towards fast prediction of electronic properties

(2014) K. T. Schütt et al.   PHYSICAL REVIEW B

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

(2013) James E. Saal et al.   JOM

A grand canonical genetic algorithm for the prediction of multi-component phase diagrams and testing of empirical potentials

(2013) William W Tipton et al.   JOURNAL OF PHYSICS-CONDENSED MATTER

Classical atomistic simulations of surfaces and heterogeneous interfaces with the charge-optimized many body (COMB) potentials

(2013) Tao Liang et al.   MATERIALS SCIENCE & ENGINEERING R-REPORTS

On representing chemical environments

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

Commentary: The Materials Project: A materials genome approach to accelerating materials innovation

(2013) Anubhav Jain et al.   APL Materials

AFLOW: An automatic framework for high-throughput materials discovery

(2012) Stefano Curtarolo et al.   COMPUTATIONAL MATERIALS SCIENCE

The Computational Materials Repository

(2012) David D. Landis et al.   COMPUTING IN SCIENCE & ENGINEERING

Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning

(2012) Matthias Rupp et al.   PHYSICAL REVIEW LETTERS