Data‐Driven Materials Science: Status, Challenges, and Perspectives

Finding the Right Bricks for Molecular Lego: A Data Mining Approach to Organic Semiconductor Design

(2019) Christian Kunkel et al.   CHEMISTRY OF MATERIALS

Bayesian inference of atomistic structure in functional materials

(2019) Milica Todorović et al.   npj Computational Materials

New tolerance factor to predict the stability of perovskite oxides and halides

(2019) Christopher J. Bartel et al.   Science Advances

Deep Learning Spectroscopy: Neural Networks for Molecular Excitation Spectra

(2019) Kunal Ghosh et al.   Advanced Science

Accurate molecular polarizabilities with coupled cluster theory and machine learning

(2019) David M. Wilkins et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

Library-Based LAMMPS Implementation of High-Dimensional Neural Network Potentials

(2019) Andreas Singraber et al.   Journal of Chemical Theory and Computation

Machine learning assisted design of high entropy alloys with desired property

(2019) Cheng Wen et al.   ACTA MATERIALIA

Chemical diversity in molecular orbital energy predictions with kernel ridge regression

(2019) Annika Stuke et al.   JOURNAL OF CHEMICAL PHYSICS

Machine learning for heterogeneous catalyst design and discovery

(2018) Bryan R. Goldsmith et al.   AICHE JOURNAL

Matminer: An open source toolkit for materials data mining

(2018) Logan Ward et al.   COMPUTATIONAL MATERIALS SCIENCE

DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics

(2018) Han Wang et al.   COMPUTER PHYSICS COMMUNICATIONS

SchNet – A deep learning architecture for molecules and materials

(2018) K. T. Schütt et al.   JOURNAL OF CHEMICAL PHYSICS

Alchemical and structural distribution based representation for universal quantum machine learning

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

wACSF—Weighted atom-centered symmetry functions as descriptors in machine learning potentials

(2018) M. Gastegger et al.   JOURNAL OF CHEMICAL PHYSICS

Making the Coupled Cluster Correlation Energy Machine-Learnable

(2018) Johannes T Margraf et al.   JOURNAL OF PHYSICAL CHEMISTRY A

Polymer Genome: A Data-Powered Polymer Informatics Platform for Property Predictions

(2018) Chiho Kim et al.   Journal of Physical Chemistry C

Predicting the Band Gaps of Inorganic Solids by Machine Learning

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

Neural Network Approach for Characterizing Structural Transformations by X-Ray Absorption Fine Structure Spectroscopy

(2018) Janis Timoshenko et al.   PHYSICAL REVIEW LETTERS

Growth Mechanism and Origin of High sp3 Content in Tetrahedral Amorphous Carbon

(2018) Miguel A. Caro 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

Opinion: Is science really facing a reproducibility crisis, and do we need it to?

(2018) Daniele Fanelli   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

Insightful classification of crystal structures using deep learning

(2018) Angelo Ziletti et al.   Nature Communications

The state of OA: a large-scale analysis of the prevalence and impact of Open Access articles

(2018) Heather Piwowar et al.   PeerJ

AFLOW-SYM: platform for the complete, automatic and self-consistent symmetry analysis of crystals

(2018) David Hicks et al.   Acta Crystallographica A-Foundation and Advances

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

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

An open experimental database for exploring inorganic materials

(2018) Andriy Zakutayev et al.   Scientific Data

Machine learning modeling of superconducting critical temperature

(2018) Valentin Stanev et al.   npj Computational Materials

Automated generation and ensemble-learned matching of X-ray absorption spectra

(2018) Chen Zheng et al.   npj Computational Materials

Machine learning hydrogen adsorption on nanoclusters through structural descriptors

(2018) Marc O. J. Jäger et al.   npj Computational Materials

Inverse design in search of materials with target functionalities

(2018) Alex Zunger   Nature Reviews Chemistry

The 2018 Materials by Design Roadmap

(2018) Kirstin Alberi et al.   JOURNAL OF PHYSICS D-APPLIED PHYSICS

NOMAD: The FAIR concept for big data-driven materials science

(2018) Claudia Draxl et al.   MRS BULLETIN

How to pick an electronic laboratory notebook

(2018) Roberta Kwok   NATURE

Machine learning for molecular and materials science

(2018) Keith T. Butler et al.   NATURE

Science without publication paywalls: cOAlition S for the realisation of full and immediate Open Access

(2018) Marc Schiltz   PLOS BIOLOGY

Materials structure genealogy and high-throughput topological classification of surfaces and 2D materials

(2018) Lauri Himanen et al.   npj Computational Materials

SISSO: A compressed-sensing method for identifying the best low-dimensional descriptor in an immensity of offered candidates

(2018) Runhai Ouyang et al.   PHYSICAL REVIEW MATERIALS

Prediction of Adsorption Energies for Chemical Species on Metal Catalyst Surfaces Using Machine Learning

(2018) Asif J. Chowdhury et al.   Journal of Physical Chemistry C

Main-group test set for materials science and engineering with user-friendly graphical tools for error analysis: Systematic benchmark of the numerical and intrinsic errors in state-of-the-art electronic-structure approximations

(2018) Igor Ying Zhang et al.   NEW JOURNAL OF PHYSICS

High-entropy high-hardness metal carbides discovered by entropy descriptors

(2018) Pranab Sarker et al.   Nature Communications

Combinatorial Material Science and Strain Engineering Enabled by Pulsed Laser Deposition Using Radially Segmented Targets

(2018) Max Kneiß et al.   ACS Combinatorial Science

Machine learning in materials design and discovery: Examples from the present and suggestions for the future

(2018) J. E. Gubernatis et al.   PHYSICAL REVIEW MATERIALS

Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations

(2018) M. Raissi et al.   JOURNAL OF COMPUTATIONAL PHYSICS

Computational screening of bulk materials with intrinsic intermediate band

(2018) Douglas J.R. Baquião et al.   COMPUTATIONAL MATERIALS SCIENCE

Supervised Machine-Learning-Based Determination of Three-Dimensional Structure of Metallic Nanoparticles

(2017) Janis Timoshenko et al.   Journal of Physical Chemistry Letters

The atomic simulation environment—a Python library for working with atoms

(2017) Ask Hjorth Larsen et al.   JOURNAL OF PHYSICS-CONDENSED MATTER

Machine learning: New tool in the box

(2017) Lenka Zdeborová   Nature Physics

Uncovering structure-property relationships of materials by subgroup discovery

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

ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost

(2017) J. S. Smith et al.   Chemical Science

Universal fragment descriptors for predicting properties of inorganic crystals

(2017) Olexandr Isayev et al.   Nature Communications

Molecular dynamics simulations reveal ligand-controlled positioning of a peripheral protein complex in membranes

(2017) Steven M. Ryckbosch et al.   Nature Communications

Bypassing the Kohn-Sham equations with machine learning

(2017) Felix Brockherde et al.   Nature Communications

A simple and efficient criterion for ready screening of potential topological insulators

(2017) Guohua Cao et al.   Science Bulletin

Machine learning unifies the modeling of materials and molecules

(2017) Albert P. Bartók et al.   Science Advances

Towards efficient data exchange and sharing for big-data driven materials science: metadata and data formats

(2017) Luca M. Ghiringhelli et al.   npj Computational Materials

Machine learning in materials informatics: recent applications and prospects

(2017) Rampi Ramprasad et al.   npj Computational Materials

A universal strategy for the creation of machine learning-based atomistic force fields

(2017) Tran Doan Huan et al.   npj Computational Materials

Organic materials database: An open-access online database for data mining

(2017) Stanislav S. Borysov et al.   PLoS One

Rational Co-Design of Polymer Dielectrics for Energy Storage

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

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

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

AiiDA: automated interactive infrastructure and database for computational science

(2016) Giovanni Pizzi et al.   COMPUTATIONAL MATERIALS SCIENCE

Materials informatics: a journey towards material design and synthesis

(2016) Keisuke Takahashi et al.   DALTON TRANSACTIONS

The Materials Data Facility: Data Services to Advance Materials Science Research

(2016) B. Blaiszik et al.   JOM

Materials Data Infrastructure: A Case Study of the Citrination Platform to Examine Data Import, Storage, and Access

(2016) Jordan O’Mara et al.   JOM

ChemDataExtractor: A Toolkit for Automated Extraction of Chemical Information from the Scientific Literature

(2016) Matthew C. Swain et al.   Journal of Chemical Information and Modeling

Machine Learning Force Fields: Construction, Validation, and Outlook

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

Virtual Screening for High Carrier Mobility in Organic Semiconductors

(2016) Christoph Schober et al.   Journal of Physical Chemistry Letters

Beyond bulk single crystals: A data format for all materials structure–property–processing relationships

(2016) Kyle Michel et al.   MRS BULLETIN

Design of efficient molecular organic light-emitting diodes by a high-throughput virtual screening and experimental approach

(2016) Rafael Gómez-Bombarelli et al.   NATURE MATERIALS

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

Reproducibility in density functional theory calculations of solids

(2016) K. Lejaeghere et al.   SCIENCE

How open science helps researchers succeed

(2016) Erin C McKiernan et al.   eLife

The FAIR Guiding Principles for scientific data management and stewardship

(2016) Mark D. Wilkinson et al.   Scientific Data

Machine learning bandgaps of double perovskites

(2016) G. Pilania et al.   Scientific Reports

Materials Data Science: Current Status and Future Outlook

(2015) Surya R. Kalidindi et al.   Annual Review of Materials Research

Materials Informatics: The Materials “Gene” and Big Data

(2015) Krishna Rajan   Annual Review of Materials Research

Materials Cartography: Representing and Mining Materials Space Using Structural and Electronic Fingerprints

(2015) Olexandr Isayev et al.   CHEMISTRY OF MATERIALS

The AFLOW standard for high-throughput materials science calculations

(2015) Camilo E. Calderon et al.   COMPUTATIONAL MATERIALS SCIENCE

A survey on knowledge representation in materials science and engineering: An ontological perspective

(2015) Xiaoming Zhang et al.   COMPUTERS IN INDUSTRY

FireWorks: a dynamic workflow system designed for high-throughput applications

(2015) Anubhav Jain et al.   CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE

Machine learning for quantum mechanics in a nutshell

(2015) Matthias Rupp   INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY

Crystal structure representations for machine learning models of formation energies

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

Machine Learning Predictions of Molecular Properties: Accurate Many-Body Potentials and Nonlocality in Chemical Space

(2015) Katja Hansen et al.   Journal of Physical Chemistry Letters

Deep learning in neural networks: An overview

(2015) Jürgen Schmidhuber   NEURAL NETWORKS

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

Properties of Schottky Barrier Diodes on (InxGa1–x)2O3 for 0.01 ≤ x ≤ 0.85 Determined by a Combinatorial Approach

(2015) H. von Wenckstern et al.   ACS Combinatorial Science

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

(2015) Scott Kirklin et al.   npj Computational Materials

A semantic knowledge management system for laminated composites

(2014) Vivek Premkumar et al.   ADVANCED ENGINEERING INFORMATICS

A RESTful API for exchanging materials data in the AFLOWLIB.org consortium

(2014) Richard H. Taylor et al.   COMPUTATIONAL MATERIALS SCIENCE

High-Throughput Calculations of Molecular Properties in the MedeA Environment: Accuracy of PM7 in Predicting Vibrational Frequencies, Ideal Gas Entropies, Heat Capacities, and Gibbs Free Energies of Organic Molecules

(2014) Xavier Rozanska et al.   JOURNAL OF CHEMICAL AND ENGINEERING DATA

Understanding the Composition and Activity of Electrocatalytic Nanoalloys in Aqueous Solvents: A Combination of DFT and Accurate Neural Network Potentials

(2014) Nongnuch Artrith et al.   NANO LETTERS

Automatic and Systematic Atomistic Simulations in the MedeA®Software Environment: Application to EU-REACH

(2014) Xavier Rozanska et al.   Oil & Gas Science and Technology-Revue d IFP Energies nouvelles

New Light-Harvesting Materials Using Accurate and Efficient Bandgap Calculations

(2014) Ivano E. Castelli et al.   Advanced Energy Materials

Error Estimates for Solid-State Density-Functional Theory Predictions: An Overview by Means of the Ground-State Elemental Crystals

(2013) K. Lejaeghere et al.   CRITICAL REVIEWS IN SOLID STATE AND MATERIALS SCIENCES

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

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

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 Computational Materials Repository

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

New cubic perovskites for one- and two-photon water splitting using the computational materials repository

(2012) Ivano E. Castelli et al.   Energy & Environmental Science

A search model for topological insulators with high-throughput robustness descriptors

(2012) Kesong Yang et al.   NATURE MATERIALS

Identification of Potential Photovoltaic Absorbers Based on First-Principles Spectroscopic Screening of Materials

(2012) Liping Yu et al.   PHYSICAL REVIEW LETTERS

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

Focus on Combinatorial Materials Science

(2012) Toyohiro Chikyo   SCIENCE AND TECHNOLOGY OF ADVANCED MATERIALS

CALPHAD, first and second generation – Birth of the materials genome

(2012) Larry Kaufman et al.   SCRIPTA MATERIALIA

CatApp: A Web Application for Surface Chemistry and Heterogeneous Catalysis

(2011) Jens S. Hummelshøj et al.   ANGEWANDTE CHEMIE-INTERNATIONAL EDITION

A high-throughput infrastructure for density functional theory calculations

(2011) Anubhav Jain et al.   COMPUTATIONAL MATERIALS SCIENCE

Computational screening of perovskite metal oxides for optimal solar light capture

(2011) Ivano E. Castelli et al.   Energy & Environmental Science

The potential of atomistic simulations and the knowledgebase of interatomic models

(2011) E. B. Tadmor et al.   JOM

Atom-centered symmetry functions for constructing high-dimensional neural network potentials

(2011) Jörg Behler   JOURNAL OF CHEMICAL PHYSICS

The International Nucleotide Sequence Database Collaboration

(2011) I. Karsch-Mizrachi et al.   NUCLEIC ACIDS RESEARCH

Simplifying the representation of complex free-energy landscapes using sketch-map

(2011) Michele Ceriotti et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

Combinatorial and High-Throughput Screening of Materials Libraries: Review of State of the Art

(2011) Radislav Potyrailo et al.   ACS Combinatorial Science

Chemoinformatics: a history

(2011) Peter Willett   Wiley Interdisciplinary Reviews-Computational Molecular Science

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

(2010) Geoffroy Hautier et al.   CHEMISTRY OF MATERIALS

High-throughput electronic band structure calculations: Challenges and tools

(2010) Wahyu Setyawan et al.   COMPUTATIONAL MATERIALS SCIENCE

Efficient and Accurate Double-Hybrid-Meta-GGA Density Functionals—Evaluation with the Extended GMTKN30 Database for General Main Group Thermochemistry, Kinetics, and Noncovalent Interactions

(2010) Lars Goerigk et al.   Journal of Chemical Theory and Computation

Gaussian Approximation Potentials: The Accuracy of Quantum Mechanics, without the Electrons

(2010) Albert P. Bartók et al.   PHYSICAL REVIEW LETTERS

Distilling Free-Form Natural Laws from Experimental Data

(2009) Michael Schmidt et al.   SCIENCE

Principal component analysis and dimensional analysis as materials informatics tools to reduce dimensionality in materials science and engineering

(2009) Krishna Rajan et al.   Statistical Analysis and Data Mining

Combinatorial Materials Sciences: Experimental Strategies for Accelerated Knowledge Discovery

(2008) Krishna Rajan   Annual Review of Materials Research