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RESEARCH PAPER · Annual Review of Materials Research · 2020

Evolving the Materials Genome: How Machine Learning Is Fueling the Next Generation of Materials Discovery

Changwon Suh, Clyde Fare, James A. Warren, Edward O. Pyzer‐Knapp

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Evolving the Materials Genome: How Machine Learning Is Fueling the Next Generation of Materials DiscoveryOriginalDevelopment and application of battery materials database2Machine-learning interatomic potentials enable first-principles multiscale modeling of lattice thermal conductivity in graphene/borophene heterostructures221Machine-learning interatomic potentials enable first-principles multiscale modeling of lattice thermal conductivity in graphene/borophene heterostructures205Enhanced Hyperbox Classifier Model for Nanomaterial Discovery10Effect of recycling on the mechanical behavior and structure of additively manufactured acrylonitrile butadiene styrene (ABS)104Distributed Multi-Objective Bayesian Optimization for the Intelligent Navigation of Energy Structure Function Maps For Efficient Property Discovery2Distributed Multi-Objective Bayesian Optimization for the Intelligent Navigation of Energy Structure Function Maps For Efficient Property Discovery3Electron channeling contrast imaging characterization and crystal plasticity modelling of dislocation activity in Ti21S BCC material11Machine learning-guided design and development of metallic structural materials27Machine learning for advanced energy materials208Machine learning the quantum-chemical properties of metal–organic frameworks for accelerated materials discovery538Machine Learning in Magnetic Materials48Machine Learning Application in Genomic, Exercise, and Vital Datasets2An Ontology-Based Approach to Enable Data-Driven Research in the Field of NDT in Civil Engineering15Machine Learning Screening of Metal-Ion Battery Electrode Materials116Machine learning on neutron and x-ray scattering and spectroscopies116The Advantage of Nanowire Configuration in Band Structure Determination9Autonomous experimentation systems for materials development: A community perspective398Accelerating computational discovery of porous solids through improved navigation of energy-structure-function maps24Balancing data for generalizable machine learning to predict glass-forming ability of ternary alloys34Deep Learning Unlocks X‐ray Microtomography Segmentation of Multiclass Microdamage in Heterogeneous Materials41Progress in Materials Data Availability and Application: A review1Guidance for Materials 4.0 to interact with a digital twin16Introduction0How can polydispersity information be integrated in the QSPR modeling of mechanical properties?5Machine Learning–Assisted Design of Material Properties80Promoting Mechanistic Understanding of Lithium Deposition and Solid‐Electrolyte Interphase (SEI) Formation Using Advanced Characterization and Simulation Methods: Recent Progress, Limitations, and Future Perspectives139Machine learning in energy storage materials106A Perspective on Digital Knowledge Representation in Materials Science and Engineering40Predicting tensorial molecular properties with equivariant machine learning models47Integrating atomistic simulations and machine learning to design multi-principal element alloys with superior elastic modulus20Accelerating materials discovery using artificial intelligence, high performance computing and robotics462Rapid design of secondary deformation-aging parameters for ultra-low Co content Cu-Ni-Co-Si-X alloy via Bayesian optimization machine learning44Human–Machine Interface1Autonomous materials discovery and manufacturing (AMDM): A review and perspectives18Innovations Toward the Valorization of Plastics Waste65Quantitative high-throughput measurement of bulk mechanical properties using commonly available equipment11Machine-Learning-Assisted Design of Highly Tough Thermosetting Polymers76Data-driven methods for discovery of next-generation electrostrictive materials12A multi-fidelity machine learning approach to high throughput materials screening77Algorithmic Settling Time: A Conceptual Framework for When Materials AI Outputs Stabilize0Conceptual Foundations for Value-Sensitive Design of Materials AI Systems0Discovery of thermosetting polymers with low hygroscopicity, low thermal expansivity, and high modulus by machine learning35Generative organic electronic molecular design informed by quantum chemistry21Recent Advance of Machine Learning in Selecting New Materials7Smart transportation based on AI and ML technology4Analysis and evaluation of machine learning applications in materials design and discovery33Introduction of Materials Genome Technology and Its Applications in the Field of Biomedical Materials11Generative Organic Electronic Molecular Design via Reinforcement Learning Integration with Quantum Chemistry: Tuning Singlet and Triplet Energy Energy Levels0Generative Organic Electronic Molecular Design via Reinforcement Learning Integration with Quantum Chemistry: Tuning Singlet and Triplet Energy Levels1Hardness and fracture toughness models by symbolic regression19Machine learning-based prediction and interpretation of decomposition temperatures of energetic materials23Megalibraries: Supercharged acceleration of materials discovery15Predicting the Composition and Mechanical Properties of Seaweed Bioplastics from the Scientific Literature: A Machine Learning Approach for Modeling Sparse Data16A cloud platform for sharing and automated analysis of raw data from high throughput polymer MD simulations12Scope of machine learning in materials research—A review230Development of photoelectrochemical water splitting photoanode: bibliometric analysis and artificial intelligence advancement15A Conceptual Blueprint for “Digital Materials Twins” without Simulation: Definitions, Boundaries, and Use-Cases0A physics-inspired approach to the understanding of molecular representations and models4A Theory of Reproducibility for AI-Generated Materials Insights: What Must Be True for Claims to Hold0An integrated screening approach for designing efficient thermally activated delayed fluorescent materials for OLEDs11Combining Bayesian optimization and automation to simultaneously optimize reaction conditions and routes29Developments and applications of the OPTIMADE API for materials discovery, design, and data exchange52Interpretability in Materials AI — What “Explanation” Means and How It Should Be Evaluated Conceptually0A process-structure-property model via physics-based/data-driven hybrid methods for freeze-cast porous ceramics in Si3N4-Si2N2O case system19A comprehensive review of biodetoxification of trichothecenes: Mechanisms, limitations and novel strategies8Advancing biomolecular simulation through exascale HPC, AI and quantum computing16Label generation with consistency on the graph for multi-label feature selection2Addressing the Accuracy-Cost Trade-off in Material Property Prediction Using a Teacher-Student Strategy5Targeted materials discovery using Bayesian algorithm execution48Machine learning-aided design of LaCe(Fe,Mn,Si)13H-type magnetocaloric materials for room-temperature applications9Fault‐tolerant quantum chemical calculations with improved machine‐learning models1Developments and applications of the OPTIMADE API for materials discovery, design, and data exchange18LiCOMPGNN - Lightweight Contextual Message Passing for Crystalline Materials Property Prediction1A Review on Data-Driven Constitutive Laws for Solids142Solutions for Lithium Battery Materials Data Issues in Machine Learning: Overview and Future Outlook31Genetic algorithm-accelerated computational discovery of liquid crystal polymers with enhanced optical properties2New Intelligent Computing Techniques for Engineering Materials—A Comprehensive Review0The Influence of Functional Modification on Wear Resistance of Graphene/Nbr Composites: Combined Molecular Dynamics and Interpretable Machine Learning0The Influence of Functional Modification on Wear Resistance of Graphene/Nbr Composites: Combined Molecular Dynamics and Interpretable Machine Learning0High Throughput Virtual Screening of Metal-Organic Frameworks for Hydrogen Storage0Dislocation cartography: Representations and unsupervised classification of dislocation networks with unique fingerprints1Active learning-assisted search for thermal storage used TiNi shape memory alloys1445High-throughput screening for novel medical materials: machine learning-enabled approaches0Enhancing energy materials with data-driven methods: A roadmap to long-term hydrogen energy sustainability using machine learning16Design and Application of Electrocatalyst Based on Machine Learning33Advances in Toughening Modification Methods for Epoxy Resins: A Comprehensive Review107Prediction of ABX3 Perovskite Formation Energy Using Machine Learning3The influence of functional modification on wear resistance of graphene/NBR composites: Combined molecular dynamics and interpretable machine learning8High-Throughput Calculations and Machine Learning for Discovering Halide-Type Solid-State Electrolyte Materials12Learning crystallographic orientations from electron backscatter diffraction patterns using variational autoencoder0MXenes for CO2 conversion to fuel: correlation between atomistic simulation, experimental and artificial intelligence0Machine learning-aided design of adapted water treatment adsorbents: Advances, challenges, and opportunities10Accelerated Discovery of High-Temperature Perovskite Oxygen Sensors via Hierarchical Machine Learning0Machine learning in next-generation AEM fuel cells: a systematic review7Image processing pipeline for AI-driven nanoparticle megalibrary characterization1AI-Driven Materials Genome: Accelerated Discovery of Multi-Functional Sensing Materials0Autonomous Materials Exploration Integrates Automated Phase Identification and AI Agents Enhanced by Human Guidance0Retardation of Cr diffusion by Re in FCC Ni–Re–Cr alloys: High-throughput experiments and CALPHAD-based mobility assessment0

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