Machine learning in materials informatics: recent applications and prospects

A rational co-design approach to the creation of new dielectric polymers with high energy density

(2017) Gregory M. Treich et al.   IEEE TRANSACTIONS ON DIELECTRICS AND ELECTRICAL INSULATION

Spherical harmonics based descriptor for neural network potentials: Structure and dynamics of Au147 nanocluster

(2017) Shweta Jindal et al.   JOURNAL OF CHEMICAL PHYSICS

Learning physical descriptors for materials science by compressed sensing

(2017) Luca M Ghiringhelli et al.   NEW JOURNAL OF PHYSICS

Uncovering structure-property relationships of materials by subgroup discovery

(2017) Bryan R Goldsmith et al.   NEW JOURNAL OF PHYSICS

Making AI More Human

(2017) Alison Gopnik   SCIENTIFIC AMERICAN

Fulfilling the promise of the materials genome initiative with high-throughput experimental methodologies

(2017) M. L. Green et al.   Applied Physics Reviews

Machine learning of accurate energy-conserving molecular force fields

(2017) Stefan Chmiela et al.   Science Advances

Rational Co-Design of Polymer Dielectrics for Energy Storage

(2016) Arun Mannodi-Kanakkithodi et al.   ADVANCED MATERIALS

High-Throughput Computational Screening of Perovskites for Thermochemical Water Splitting Applications

(2016) Antoine A. Emery et al.   CHEMISTRY OF MATERIALS

Three-Parameter Crystal-Structure Prediction for sp-d-Valent Compounds

(2016) Arthur F. Bialon et al.   CHEMISTRY OF MATERIALS

From Organized High-Throughput Data to Phenomenological Theory using Machine Learning: The Example of Dielectric Breakdown

(2016) Chiho Kim et al.   CHEMISTRY OF MATERIALS

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

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

Semi-Supervised Approach to Phase Identification from Combinatorial Sample Diffraction Patterns

(2016) Jonathan Kenneth Bunn et al.   JOM

Machine Learning Assisted Predictions of Intrinsic Dielectric Breakdown Strength of ABX3 Perovskites

(2016) Chiho Kim et al.   Journal of Physical Chemistry C

Machine Learning Force Fields: Construction, Validation, and Outlook

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

Modelling defects in Ni–Al with EAM and DFT calculations

(2016) F Bianchini et al.   MODELLING AND SIMULATION IN MATERIALS SCIENCE AND ENGINEERING

Role of materials data science and informatics in accelerated materials innovation

(2016) Surya R. Kalidindi et al.   MRS BULLETIN

Comparing molecules and solids across structural and alchemical space

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

Machine Learning Energies of 2 Million Elpasolite(ABC2D6)Crystals

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

Accelerated search for BaTiO3-based piezoelectrics with vertical morphotropic phase boundary using Bayesian learning

(2016) Dezhen Xue et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

Advanced polymeric dielectrics for high energy density applications

(2016) Tran Doan Huan et al.   PROGRESS IN MATERIALS SCIENCE

Use of Bayesian Inference in Crystallographic Structure Refinement via Full Diffraction Profile Analysis

(2016) Chris M. Fancher et al.   Scientific Reports

Predicting defect behavior in B2 intermetallics by merging ab initio modeling and machine learning

(2016) Bharat Medasani et al.   npj Computational Materials

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 Strategy for Accelerated Design of Polymer Dielectrics

(2016) Arun Mannodi-Kanakkithodi et al.   Scientific Reports

Machine learning bandgaps of double perovskites

(2016) G. Pilania et al.   Scientific Reports

Structure–property linkages using a data science approach: Application to a non-metallic inclusion/steel composite system

(2015) Akash Gupta et al.   ACTA MATERIALIA

Nonlinear gradient denoising: Finding accurate extrema from inaccurate functional derivatives

(2015) John C. Snyder et al.   INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY

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

Spectral neighbor analysis method for automated generation of quantum-accurate interatomic potentials

(2015) A.P. Thompson et al.   JOURNAL OF COMPUTATIONAL PHYSICS

Descriptors of Oxygen-Evolution Activity for Oxides: A Statistical Evaluation

(2015) Wesley T. Hong et al.   Journal of Physical Chemistry C

High-throughput determination of structural phase diagram and constituent phases using GRENDEL

(2015) A G Kusne et al.   NANOTECHNOLOGY

Application of data science tools to quantify and distinguish between structures and models in molecular dynamics datasets

(2015) Surya R Kalidindi et al.   NANOTECHNOLOGY

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

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

Learning scheme to predict atomic forces and accelerate materials simulations

(2015) V. Botu et al.   PHYSICAL REVIEW B

Accelerated materials property predictions and design using motif-based fingerprints

(2015) Tran Doan Huan 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

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

(2015) Zhenwei Li et al.   PHYSICAL REVIEW LETTERS

Big Data of Materials Science: Critical Role of the Descriptor

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

Multi-fidelity modelling via recursive co-kriging and Gaussian–Markov random fields

(2015) P. Perdikaris et al.   PROCEEDINGS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES

Machine learning: Trends, perspectives, and prospects

(2015) M. I. Jordan et al.   SCIENCE

Adaptive machine learning framework to accelerateab initiomolecular dynamics

(2014) Venkatesh Botu et al.   INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY

Rapid and Accurate Machine Learning Recognition of High Performing Metal Organic Frameworks for CO2 Capture

(2014) Michael Fernandez et al.   Journal of Physical Chemistry Letters

Representing potential energy surfaces by high-dimensional neural network potentials

(2014) J Behler   JOURNAL OF PHYSICS-CONDENSED MATTER

A genomic approach to the stability, elastic, and electronic properties of the MAX phases

(2014) Sitaram Aryal et al.   PHYSICA STATUS SOLIDI B-BASIC SOLID STATE PHYSICS

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

Accuracy and transferability of Gaussian approximation potential models for tungsten

(2014) Wojciech J. Szlachta 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

Rational design of all organic polymer dielectrics

(2014) Vinit Sharma et al.   Nature Communications

Informatics-aided bandgap engineering for solar materials

(2013) Partha Dey et al.   COMPUTATIONAL MATERIALS SCIENCE

Orbital-free bond breaking via machine learning

(2013) John C. Snyder et al.   JOURNAL OF CHEMICAL PHYSICS

On representing chemical environments

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

The rational design of polyurea & polyurethane dielectric materials

(2013) R.G. Lorenzini et al.   POLYMER

Accelerating materials property predictions using machine learning

(2013) Ghanshyam Pilania et al.   Scientific Reports

Key computational modeling issues in Integrated Computational Materials Engineering

(2012) Jitesh H. Panchal et al.   COMPUTER-AIDED DESIGN

How Critical Are the van der Waals Interactions in Polymer Crystals?

(2012) Chun-Sheng Liu et al.   JOURNAL OF PHYSICAL CHEMISTRY A

The Knowledge Gradient Algorithm for a General Class of Online Learning Problems

(2012) Ilya O. Ryzhov et al.   OPERATIONS RESEARCH

Finding Density Functionals with Machine Learning

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

Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning

(2012) Matthias Rupp et al.   PHYSICAL REVIEW LETTERS

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

(2010) Geoffroy Hautier et al.   CHEMISTRY OF MATERIALS

Building effective models from sparse but precise data: Application to an alloy cluster expansion model

(2010) Eric Cockayne et al.   PHYSICAL REVIEW B

Exact expressions for structure selection in cluster expansions

(2010) Tim Mueller 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

Bayesian approach to cluster expansions

(2009) Tim Mueller et al.   PHYSICAL REVIEW B

Cluster expansion method for multicomponent systems based on optimal selection of structures for density-functional theory calculations

(2009) Atsuto Seko et al.   PHYSICAL REVIEW B

Distilling Free-Form Natural Laws from Experimental Data

(2009) Michael Schmidt et al.   SCIENCE

Metadynamics Simulations of the High-Pressure Phases of Silicon Employing a High-Dimensional Neural Network Potential

(2008) Jörg Behler et al.   PHYSICAL REVIEW LETTERS