Direct Prediction of Phonon Density of States With Euclidean Neural Networks

Unsupervised Machine Learning and Band Topology

(2020) Mathias S. Scheurer et al.   PHYSICAL REVIEW LETTERS

Five thermal energy grand challenges for decarbonization

(2020) Asegun Henry et al.   Nature Energy

Bandgap prediction by deep learning in configurationally hybridized graphene and boron nitride

(2019) Yuan Dong et al.   npj Computational Materials

Active learning of uniformly accurate interatomic potentials for materials simulation

(2019) Linfeng Zhang et al.   PHYSICAL REVIEW MATERIALS

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

(2019) Chi Chen et al.   CHEMISTRY OF MATERIALS

Predicting interfacial thermal resistance by machine learning

(2019) Yen-Ju Wu et al.   npj Computational Materials

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

(2019) Jonathan Schmidt et al.   npj Computational Materials

Predicting the Band Gaps of Inorganic Solids by Machine Learning

(2018) Ya Zhuo et al.   Journal of Physical Chemistry 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

High-throughput density-functional perturbation theory phonons for inorganic materials

(2018) Guido Petretto et al.   Scientific Data

Machine learning modeling of superconducting critical temperature

(2018) Valentin Stanev et al.   npj Computational Materials

Controlling an organic synthesis robot with machine learning to search for new reactivity

(2018) Jarosław M. Granda et al.   NATURE

Chimera: enabling hierarchy based multi-objective optimization for self-driving laboratories

(2018) Florian Häse et al.   Chemical Science

Vibrational Properties of Metastable Polymorph Structures by Machine Learning

(2018) Fleur Legrain et al.   Journal of Chemical Information and Modeling

Electron-phonon interactions from first principles

(2017) Feliciano Giustino   REVIEWS OF MODERN PHYSICS

Universal fragment descriptors for predicting properties of inorganic crystals

(2017) Olexandr Isayev et al.   Nature Communications

Machine learning of accurate energy-conserving molecular force fields

(2017) Stefan Chmiela et al.   Science Advances

Rethinking phonons: The issue of disorder

(2017) Hamid Reza Seyf et al.   npj Computational Materials

Machine learning in materials informatics: recent applications and prospects

(2017) Rampi Ramprasad et al.   npj Computational Materials

Energy-free machine learning force field for aluminum

(2017) Ivan Kruglov et al.   Scientific Reports

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

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

Machine Learning Force Fields: Construction, Validation, and Outlook

(2016) V. Botu et al.   Journal of Physical Chemistry C

Machine-learning-assisted materials discovery using failed experiments

(2016) Paul Raccuglia et al.   NATURE

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

(2016) Logan Ward et al.   npj Computational Materials

Molecular Dynamics with On-the-Fly Machine Learning of Quantum-Mechanical Forces

(2015) Zhenwei Li et al.   PHYSICAL REVIEW LETTERS

Hydrogen bonding, halogen bonding and lithium bonding: an atoms in molecules and natural bond orbital perspective towards conservation of total bond order, inter- and intra-molecular bonding

(2014) Abhishek Shahi et al.   PHYSICAL CHEMISTRY CHEMICAL PHYSICS

Phonon density of states and anharmonicity ofUO2

(2014) Judy W. L. Pang et al.   PHYSICAL REVIEW B

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

Large phonon band gap inSrTiO3and the vibrational signatures of ferroelectricity inATiO3perovskites: First-principles lattice dynamics and inelastic neutron scattering

(2008) Narayani Choudhury et al.   PHYSICAL REVIEW B