Developing an improved crystal graph convolutional neural network framework for accelerated materials discovery

Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals

(2019) Chi Chen et al.   CHEMISTRY OF MATERIALS

SchNet – A deep learning architecture for molecules and materials

(2018) K. T. Schütt 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

Hierarchical visualization of materials space with graph convolutional neural networks

(2018) Tian Xie et al.   JOURNAL OF CHEMICAL PHYSICS

ElemNet: Deep Learning the Chemistry of Materials From Only Elemental Composition

(2018) Dipendra Jha et al.   Scientific Reports

Machine-learning-accelerated high-throughput materials screening: Discovery of novel quaternary Heusler compounds

(2018) Kyoungdoc Kim et al.   PHYSICAL REVIEW MATERIALS

Quantum-chemical insights from deep tensor neural networks

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

Universal fragment descriptors for predicting properties of inorganic crystals

(2017) Olexandr Isayev et al.   Nature Communications

High-Throughput Machine-Learning-Driven Synthesis of Full-Heusler Compounds

(2016) Anton O. Oliynyk et al.   CHEMISTRY OF MATERIALS

Molecular graph convolutions: moving beyond fingerprints

(2016) Steven Kearnes et al.   JOURNAL OF COMPUTER-AIDED MOLECULAR DESIGN

Machine Learning Energies of 2 Million Elpasolite(ABC2D6)Crystals

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

Predictive analytics for crystalline materials: bulk modulus

(2016) Al'ona Furmanchuk et al.   RSC Advances

A Bayesian approach to calibrating high-throughput virtual screening results and application to organic photovoltaic materials

(2016) Edward O. Pyzer-Knapp et al.   Materials Horizons

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

(2016) Logan Ward et al.   npj Computational Materials

Finding New Perovskite Halides via Machine Learning

(2016) Ghanshyam Pilania et al.   Frontiers in 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

Mining for elastic constants of intermetallics from the charge density landscape

(2015) Chang Sun Kong et al.   PHYSICA B-CONDENSED MATTER

Structure classification and melting temperature prediction in octet AB solids via machine learning

(2015) G. Pilania et al.   PHYSICAL REVIEW B

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

(2015) Atsuto Seko et al.   PHYSICAL REVIEW LETTERS

Big Data of Materials Science: Critical Role of the Descriptor

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

More statistics on intermetallic compounds – ternary phases

(2015) Julia Dshemuchadse et al.   Acta Crystallographica A-Foundation and Advances

The Open Quantum Materials Database (OQMD): assessing the accuracy of DFT formation energies

(2015) Scott Kirklin et al.   npj Computational Materials

Machine learning with systematic density-functional theory calculations: Application to melting temperatures of single- and binary-component solids

(2014) Atsuto Seko et al.   PHYSICAL REVIEW B

Combinatorial screening for new materials in unconstrained composition space with machine learning

(2014) B. Meredig et al.   PHYSICAL REVIEW B

Informatics-aided bandgap engineering for solar materials

(2013) Partha Dey et al.   COMPUTATIONAL 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

The high-throughput highway to computational materials design

(2013) Stefano Curtarolo et al.   NATURE MATERIALS

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

(2013) Anubhav Jain et al.   APL Materials

AFLOWLIB.ORG: A distributed materials properties repository from high-throughput ab initio calculations

(2012) Stefano Curtarolo et al.   COMPUTATIONAL MATERIALS SCIENCE

Python Materials Genomics (pymatgen): A robust, open-source python library for materials analysis

(2012) Shyue Ping Ong et al.   COMPUTATIONAL MATERIALS SCIENCE

The Harvard Clean Energy Project: Large-Scale Computational Screening and Design of Organic Photovoltaics on the World Community Grid

(2011) Johannes Hachmann et al.   Journal of Physical Chemistry Letters