Materials science in the artificial intelligence age: high-throughput library generation, machine learning, and a pathway from correlations to the underpinning physics

Learning from Imperfections: Predicting Structure and Thermodynamics from Atomic Imaging of Fluctuations

(2019) Lukas Vlcek et al.   ACS Nano

Active learning in materials science with emphasis on adaptive sampling using uncertainties for targeted design

(2019) Turab Lookman et al.   npj Computational Materials

Deep learning analysis of defect and phase evolution during electron beam-induced transformations in WS2

(2019) Artem Maksov et al.   npj Computational Materials

Deep neural networks for understanding noisy data applied to physical property extraction in scanning probe microscopy

(2019) Nikolay Borodinov et al.   npj Computational Materials

New frontiers for the materials genome initiative

(2019) Juan J. de Pablo et al.   npj Computational Materials

Fast and interpretable classification of small X-ray diffraction datasets using data augmentation and deep neural networks

(2019) Felipe Oviedo et al.   npj Computational Materials

Accelerated Discovery of Large Electrostrains in BaTiO3 -Based Piezoelectrics Using Active Learning

(2018) Ruihao Yuan et al.   ADVANCED MATERIALS

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

Constant size descriptors for accurate machine learning models of molecular properties

(2018) Christopher R. Collins et al.   JOURNAL OF CHEMICAL PHYSICS

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

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

Two-dimensional materials from high-throughput computational exfoliation of experimentally known compounds

(2018) Nicolas Mounet et al.   Nature Nanotechnology

Equilibrium free energies from non-equilibrium trajectories with relaxation fluctuation spectroscopy

(2018) David Ross et al.   Nature Physics

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

(2018) Tian Xie et al.   PHYSICAL REVIEW LETTERS

Dr. Probe: A software for high-resolution STEM image simulation

(2018) J. Barthel   ULTRAMICROSCOPY

The TensorMol-0.1 model chemistry: a neural network augmented with long-range physics

(2018) Kun Yao et al.   Chemical Science

Large-scale comparison of machine learning methods for drug target prediction on ChEMBL

(2018) Andreas Mayr et al.   Chemical Science

VAMPnets for deep learning of molecular kinetics

(2018) Andreas Mardt et al.   Nature Communications

Ultrafast current imaging by Bayesian inversion

(2018) S. Somnath et al.   Nature Communications

Multi-objective Optimization for Materials Discovery via Adaptive Design

(2018) Abhijith M. Gopakumar et al.   Scientific Reports

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

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

Efficient Syntheses of Diverse, Medicinally Relevant Targets Planned by Computer and Executed in the Laboratory

(2018) Tomasz Klucznik et al.   Chem

Machine learning–enabled identification of material phase transitions based on experimental data: Exploring collective dynamics in ferroelectric relaxors

(2018) Linglong Li et al.   Science Advances

Accelerated discovery of metallic glasses through iteration of machine learning and high-throughput experiments

(2018) Fang Ren et al.   Science Advances

Machine learning modeling of superconducting critical temperature

(2018) Valentin Stanev et al.   npj Computational Materials

Reconstructing phase diagrams from local measurements via Gaussian processes: mapping the temperature-composition space to confidence

(2018) Dhiren K. Pradhan et al.   npj Computational Materials

High-throughput assessment of vacancy formation and surface energies of materials using classical force-fields

(2018) Kamal Choudhary et al.   JOURNAL OF PHYSICS-CONDENSED MATTER

Machine learning for molecular and materials science

(2018) Keith T. Butler et al.   NATURE

Networking chemical robots for reaction multitasking

(2018) Dario Caramelli et al.   Nature Communications

Machine Learning Enabled Computational Screening of Inorganic Solid Electrolytes for Suppression of Dendrite Formation in Lithium Metal Anodes

(2018) Zeeshan Ahmad et al.   ACS Central Science

Active learning for accelerated design of layered materials

(2018) Lindsay Bassman et al.   npj Computational Materials

Autonomous efficient experiment design for materials discovery with Bayesian model averaging

(2018) Anjana Talapatra et al.   PHYSICAL REVIEW MATERIALS

Consistent Integration of Experimental and Ab Initio Data into Effective Physical Models

(2017) Lukas Vlcek et al.   Journal of Chemical Theory and Computation

Prediction Errors of Molecular Machine Learning Models Lower than Hybrid DFT Error

(2017) Felix A. Faber et al.   Journal of Chemical Theory and Computation

Smart manufacturing must embrace big data

(2017) Andrew Kusiak   NATURE

Robert Le Rossignol, 1884–1976: Engineer of the ‘Haber’ process

(2017) Deri Sheppard   Notes and Records-The Royal Society Journal of the History of Science

Solar fuels photoanode materials discovery by integrating high-throughput theory and experiment

(2017) Qimin Yan et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

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

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

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

Robert Le Rossignol, 1884–1976: Engineer of the ‘Haber’ process

(2017) Deri Sheppard   Notes and Records-The Royal Society Journal of the History of Science

Evaluation and comparison of classical interatomic potentials through a user-friendly interactive web-interface

(2017) Kamal Choudhary et al.   Scientific Data

Machine learning of accurate energy-conserving molecular force fields

(2017) Stefan Chmiela et al.   Science Advances

Learning surface molecular structures via machine vision

(2017) Maxim Ziatdinov 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

Discovering charge density functionals and structure-property relationships with PROPhet: A general framework for coupling machine learning and first-principles methods

(2017) Brian Kolb et al.   Scientific Reports

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

(2017) Kamal Choudhary et al.   Scientific Reports

Chemical State Evolution in Ferroelectric Films during Tip-Induced Polarization and Electroresistive Switching

(2016) Anton V. Ievlev et al.   ACS Applied Materials & Interfaces

Characterization of Few-Layer 1T′ MoTe2 by Polarization-Resolved Second Harmonic Generation and Raman Scattering

(2016) Ryan Beams et al.   ACS Nano

High-throughput computational search for strengthening precipitates in alloys

(2016) S. Kirklin et al.   ACTA MATERIALIA

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

Amp : A modular approach to machine learning in atomistic simulations

(2016) Alireza Khorshidi et al.   COMPUTER PHYSICS COMMUNICATIONS

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

(2016) Jonathan Kenneth Bunn et al.   JOM

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

(2016) B. Blaiszik et al.   JOM

Molecular graph convolutions: moving beyond fingerprints

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

Machine Learning Force Fields: Construction, Validation, and Outlook

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

Ultrafast Nanoimaging of the Photoinduced Phase Transition Dynamics in VO2

(2016) Sven A. Dönges et al.   NANO LETTERS

Quantification of surface displacements and electromechanical phenomena via dynamic atomic force microscopy

(2016) Nina Balke et al.   NANOTECHNOLOGY

Rapid mapping of polarization switching through complete information acquisition

(2016) Suhas Somnath et al.   Nature Communications

Accelerated search for materials with targeted properties by adaptive design

(2016) Dezhen Xue et al.   Nature Communications

Automated Phase Mapping with AgileFD and its Application to Light Absorber Discovery in the V–Mn–Nb Oxide System

(2016) Santosh K. Suram et al.   ACS Combinatorial Science

A polymer dataset for accelerated property prediction and design

(2016) Tran Doan Huan et al.   Scientific Data

Autonomy in materials research: a case study in carbon nanotube growth

(2016) Pavel Nikolaev 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

Full data acquisition in Kelvin Probe Force Microscopy: Mapping dynamic electric phenomena in real space

(2016) Liam Collins 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

Quantitative Analysis of the Local Phase Transitions Induced by Laser Heating

(2015) Anton V. Ievlev et al.   ACS Nano

Learning from the Harvard Clean Energy Project: The Use of Neural Networks to Accelerate Materials Discovery

(2015) Edward O. Pyzer-Knapp et al.   ADVANCED FUNCTIONAL MATERIALS

Materials Data Science: Current Status and Future Outlook

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

Non-equilibrium alloying controls optoelectronic properties in Cu2O thin films for photovoltaic absorber applications

(2015) A. Zakutayev et al.   APPLIED PHYSICS LETTERS

Full information acquisition in piezoresponse force microscopy

(2015) Suhas Somnath et al.   APPLIED PHYSICS LETTERS

Quantitative measurements of electromechanical response with a combined optical beam and interferometric atomic force microscope

(2015) Aleksander Labuda et al.   APPLIED PHYSICS LETTERS

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

(2015) Olexandr Isayev et al.   CHEMISTRY OF MATERIALS

Interface Stability in Solid-State Batteries

(2015) William D. Richards et al.   CHEMISTRY OF MATERIALS

Gaussian approximation potentials: A brief tutorial introduction

(2015) Albert P. Bartók et al.   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

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

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

Generalized machine learning technique for automatic phase attribution in time variant high-throughput experimental studies

(2015) Jonathan Kenneth Bunn et al.   JOURNAL OF MATERIALS RESEARCH

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

(2015) A G Kusne et al.   NANOTECHNOLOGY

Deep learning

(2015) Yann LeCun et al.   NATURE

Big–deep–smart data in imaging for guiding materials design

(2015) Sergei V. Kalinin et al.   NATURE MATERIALS

Fears of an AI pioneer

(2015) J. Bohannon   SCIENCE

Identification of phases, symmetries and defects through local crystallography

(2015) Alex Belianinov et al.   Nature Communications

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

(2015) Scott Kirklin et al.   npj Computational Materials

Insights into Diffusion Mechanisms in P2 Layered Oxide Materials by First-Principles Calculations

(2014) Yifei Mo et al.   CHEMISTRY OF MATERIALS

The Materials Genome Initiative, the interplay of experiment, theory and computation

(2014) Juan J. de Pablo et al.   CURRENT OPINION IN SOLID STATE & MATERIALS SCIENCE

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

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

Rational design of all organic polymer dielectrics

(2014) Vinit Sharma et al.   Nature Communications

On-the-fly machine-learning for high-throughput experiments: search for rare-earth-free permanent magnets

(2014) Aaron Gilad Kusne et al.   Scientific Reports

Quantum chemistry structures and properties of 134 kilo molecules

(2014) Raghunathan Ramakrishnan et al.   Scientific Data

Understanding Scanning Tunneling Microscopy Contrast Mechanisms on Metal Oxides: A Case Study

(2013) Harry Mönig et al.   ACS Nano

Applications of high throughput (combinatorial) methodologies to electronic, magnetic, optical, and energy-related materials

(2013) Martin L. Green et al.   JOURNAL OF APPLIED PHYSICS

On representing chemical environments

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

Correlative Multimodal Probing of Ionically-Mediated Electromechanical Phenomena in Simple Oxides

(2013) Yunseok Kim et al.   Scientific Reports

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

Graphics processing unit (GPU) programming strategies and trends in GPU computing

(2012) André R. Brodtkorb et al.   JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING

Quantitative Viscoelastic Mapping of Polyolefin Blends with Contact Resonance Atomic Force Microscopy

(2012) Dalia G. Yablon et al.   MACROMOLECULES

Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning

(2012) Matthias Rupp et al.   PHYSICAL REVIEW LETTERS

X-ray nanodiffraction of tilted domains in a poled epitaxial BiFeO3 thin film

(2011) S. O. Hruszkewycz et al.   APPLIED PHYSICS LETTERS

Microstructure informatics using higher-order statistics and efficient data-mining protocols

(2011) Surya R. Kalidindi et al.   JOM

A wavelet transform algorithm for peak detection and application to powder x-ray diffraction data

(2011) John M. Gregoire et al.   REVIEW OF SCIENTIFIC INSTRUMENTS

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

(2010) Geoffroy Hautier et al.   CHEMISTRY OF MATERIALS

Direct evidence of mesoscopic dynamic heterogeneities at the surfaces of ergodic ferroelectric relaxors

(2010) S. V. Kalinin et al.   PHYSICAL REVIEW B

The Robot Scientist Adam

(2009) Ross D. King et al.   COMPUTER

Functional recognition imaging using artificial neural networks: applications to rapid cellular identification via broadband electromechanical response

(2009) M P Nikiforov et al.   NANOTECHNOLOGY

Battery materials for ultrafast charging and discharging

(2009) Byoungwoo Kang et al.   NATURE

Disorder Identification in Hysteresis Data: Recognition Analysis of the Random-Bond–Random-Field Ising Model

(2009) O. S. Ovchinnikov et al.   PHYSICAL REVIEW LETTERS

Rapid identification of structural phases in combinatorial thin-film libraries using x-ray diffraction and non-negative matrix factorization

(2009) C. J. Long et al.   REVIEW OF SCIENTIFIC INSTRUMENTS

An Efficient Pan-Sharpening Method via a Combined Adaptive PCA Approach and Contourlets

(2008) Vijay P. Shah et al.   IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING

High throughput thin film materials science

(2008) Z. H. Barber et al.   MATERIALS SCIENCE AND TECHNOLOGY

Microstructure reconstructions from 2-point statistics using phase-recovery algorithms

(2007) David T. Fullwood et al.   ACTA MATERIALIA

Data Analysis in Combinatorial Experiments: Applying Supervised Principal Component Technique to Investigate the Relationship Between ToF-SIMS Spectra and the Composition Distribution of Ternary Metallic Alloy Thin Films

(2007) Rossana Dell'Anna et al.   Quantitative structure-activity relationships & combinatorial science