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RESEARCH PAPER · Nature Reviews Chemistry · 2018

Inverse design in search of materials with target functionalities

Alex Zunger

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Inverse design in search of materials with target functionalitiesOriginalAdvances in thermoelectrics591Intelligent nanophotonics: merging photonics and artificial intelligence at the nanoscale302Accelerating Materials Development via Automation, Machine Learning, and High-Performance Computing340Inverse molecular design using machine learning: Generative models for matter engineering2048Assessing High-Throughput Descriptors for Prediction of Transparent Conductors86Design‐to‐Device Approach Affords Panchromatic Co‐Sensitized Solar Cells61The role of decomposition reactions in assessing first-principles predictions of solid stability115Structure dependent optoelectronic properties of monolayer antimonene, bismuthene and their binary compound59Finding the Right Bricks for Molecular Legos: A Data Mining Approach to Organic Semiconductor Design61Discovery of TaFeSb-based half-Heuslers with high thermoelectric performance386A meta-analysis of catalytic literature data reveals property-performance correlations for the OCM reaction91Materials informatics: From the atomic-level to the continuum189From DFT to machine learning: recent approaches to materials science–a review848Beware of plausible predictions of fantasy materials82Chitosan/polyvinyl alcohol nanofibrous membranes: towards green super-adsorbents for toxic gases58Energy, Phonon, and Dynamic Stability Criteria of Two-Dimensional Materials137Chemical diversity in molecular orbital energy predictions with kernel ridge regression78Chemical diversity in molecular orbital energy predictions with kernel ridge regression70Discovery of new materials using combinatorial synthesis and high-throughput characterization of thin-film materials libraries combined with computational methods357Robocrystallographer: automated crystal structure text descriptions and analysis107Recent Advances in Intensified Ethylene Production—A Review509Data‐Driven Materials Science: Status, Challenges, and Perspectives700Text-mined dataset of inorganic materials synthesis recipes258Machine learning-based design of porous graphene with low thermal conductivity99Machine Learning Approaches for Thermoelectric Materials Research231Machine Learning for Catalysis Informatics: Recent Applications and Prospects611Attribute driven inverse materials design using deep learning Bayesian framework62Generative Models for Automatic Chemical Design88Machine-enabled inverse design of inorganic solid materials: promises and challenges190Metaheuristic-based inverse design of materials – A survey46Computational functionality‐driven design of semiconductors for optoelectronic applications58Progress in Computational and Machine‐Learning Methods for Heterogeneous Small‐Molecule Activation70Uncertainty-Quantified Hybrid Machine Learning/Density Functional Theory High Throughput Screening Method for Crystals49Evolving the Materials Genome: How Machine Learning Is Fueling the Next Generation of Materials Discovery99Opportunities and Challenges for Machine Learning in Materials Science522Above Room Temperature Ferromagnetism in Gd 2 B 2 Monolayer with High Magnetic Anisotropy46The interplay between thermodynamics and kinetics in the solid-state synthesis of layered oxides264Exploring chemical compound space with quantum-based machine learning367The 2020 magnetism roadmap219The Rashba Scale: Emergence of Band Anti-crossing as a Design Principle for Materials with Large Rashba Coefficient52Insulators for 2D nanoelectronics: the gap to bridge551A critical examination of compound stability predictions from machine-learned formation energies268Inverse design of two-dimensional graphene/h-BN hybrids by a regressional and conditional GAN61How machine learning can help select capping layers to suppress perovskite degradation188Designing and understanding light-harvesting devices with machine learning112High-throughput design of magnetic materials52Array-Level Inverse Design of Beam Steering Active Metasurfaces106Machine Learning for Advanced Additive Manufacturing269Chemistry of Quantum Spin Liquids185Autonomous Discovery of Battery Electrolytes with Robotic Experimentation and Machine Learning186Inverse design of composite metal oxide optical materials based on deep transfer learning and global optimization40False metals, real insulators, and degenerate gapped metals80A critical examination of compound stability predictions from machine-learned formation energies194Data-driven algorithms for inverse design of polymers100Differentiable sampling of molecular geometries with uncertainty-based adversarial attacks67Finely tuned inverse design of metal–organic frameworks with user-desired Xe/Kr selectivity40Machine learning of solvent effects on molecular spectra and reactions174Prediction of low-Z collinear and noncollinear antiferromagnetic compounds having momentum-dependent spin splitting even without spin-orbit coupling178Designing heat transfer pathways for advanced thermoregulatory textiles111Prediction of low-Z collinear and noncollinear antiferromagnetic compounds having momentum-dependent spin splitting even without spin-orbit coupling259Machine learning in materials science: From explainable predictions to autonomous design280Machine learning augmented predictive and generative model for rupture life in ferritic and austenitic steels53Computational Discovery of New 2D Materials Using Deep Learning Generative Models84Heisenberg and anisotropic exchange interactions in magnetic materials with correlated electronic structure and significant spin-orbit coupling46Analysis of Updated Literature Data up to 2019 on the Oxidative Coupling of Methane Using an Extrapolative Machine‐Learning Method to Identify Novel Catalysts77Perspective on integrating machine learning into computational chemistry and materials science203The data-intensive scientific revolution occurring where two-dimensional materials meet machine learning62Predictive Synthesis53Autonomous reinforcement learning agent for chemical vapor deposition synthesis of quantum materials45The evolution of Materials Acceleration Platforms: toward the laboratory of the future with AMANDA70Learning hidden elasticity with deep neural networks85Machine learning for materials discovery: Two-dimensional topological insulators71Differentiable sampling of molecular geometries with uncertainty-based adversarial attacks77Different shapes of spin textures as a journey through the Brillouin zone81Inverse design of glass structure with deep graph neural networks55Relationships between Distortions of Inorganic Framework and Band Gap of Layered Hybrid Halide Perovskites68Photocatalytic Z‐Scheme Overall Water Splitting: Recent Advances in Theory and Experiments291Bayesian optimization for chemical products and functional materials172Machine‐Learning Microstructure for Inverse Material Design116Artificial Intelligence for Autonomous Molecular Design: A Perspective50On-the-fly interpretable machine learning for rapid discovery of two-dimensional ferromagnets with high Curie temperature92Inverse Design of Nanoparticles Using Multi‐Target Machine Learning41Inverse design of two-dimensional materials with invertible neural networks69An invertible crystallographic representation for general inverse design of inorganic crystals with targeted properties189Advancing statistical learning and artificial intelligence in nanophotonics inverse design48Selected machine learning of HOMO–LUMO gaps with improved data-efficiency49Autonomous Reaction Network Exploration in Homogeneous and Heterogeneous Catalysis69Experimental Synthesis of Theoretically Predicted Multivalent Ternary Nitride Materials73Review of computational approaches to predict the thermodynamic stability of inorganic solids196Inverse design of 3d molecular structures with conditional generative neural networks188Inverse Design of Materials by Machine Learning136Application of deep neural network learning in composites design64Distributed representations of atoms and materials for machine learning41Machine learning in energy storage materials106Recent progress in the machine learning-assisted rational design of alloys69Recent advances and applications of deep learning methods in materials science1161Data‐Driven Materials Innovation and Applications186MatSciBERT: A materials domain language model for text mining and information extraction342Beyond T-graphene: Two-dimensional tetragonal allotropes and their potential applications76Human- and machine-centred designs of molecules and materials for sustainability and decarbonization137Why big data and compute are not necessarily the path to big materials science51Machine learning for high-entropy alloys: Progress, challenges and opportunities396High-Throughput Inverse Design for 2D Ferroelectric Rashba Semiconductors42Efficient hyperparameter tuning for kernel ridge regression with Bayesian optimization104Machine learning accelerates the materials discovery142Testing the r 2 SCAN Density Functional for the Thermodynamic Stability of Solids with and without a van der Waals Correction90Classification of properties and their relation to chemical bonding: Essential steps toward the inverse design of functional materials56Graph neural networks for materials science and chemistry860Revisiting the Nature of Chemical Bonding in Chalcogenides to Explain and Design their Properties158Deep reinforcement learning empowers automated inverse design and optimization of photonic crystals for nanoscale laser cavities70Machine learning-inspired battery material innovation49Recent Advances in Thermoregulatory Clothing: Materials, Mechanisms, and Perspectives226Polymer Informatics at Scale with Multitask Graph Neural Networks122A robotic platform for the synthesis of colloidal nanocrystals168The role of machine learning in carbon neutrality: Catalyst property prediction, design, and synthesis for carbon dioxide reduction61Advances in machine learning- and artificial intelligence-assisted material design of steels109Membrane Science Meets Machine Learning: Future and Potential Use in Assisting Membrane Material Design and Fabrication52Graded honeycombs with high impact resistance through machine learning-based optimization42Converting Nanotoxicity Data to Information Using Artificial Intelligence and Simulation85Inverse design and AI/Deep generative networks in food design: A comprehensive review59Deep Learning in Mechanical Metamaterials: From Prediction and Generation to Inverse Design371Accelerating drug target inhibitor discovery with a deep generative foundation model51Data‐driven and artificial intelligence accelerated steel material research and intelligent manufacturing technology78Artificial intelligence and machine learning in the design and additive manufacturing of responsive composites54Functional Alkali Metal-Based Ternary Chalcogenides: Design, Properties, and Opportunities50In Pursuit of the Exceptional: Research Directions for Machine Learning in Chemical and Materials Science99Accelerating the prediction of stable materials with machine learning104The design and optimization of heterogeneous catalysts using computational methods61Machine Learning for Polymer Design to Enhance Pervaporation-Based Organic Recovery47Nonrelativistic spin splittings and altermagnetism in twisted bilayers of centrosymmetric antiferromagnets54Data-driven inverse design of flexible pressure sensors58Tailor-Made Polysaccharides for Biomedical Applications63Structure-based out-of-distribution (OOD) materials property prediction: a benchmark study66Accelerating Computational Materials Discovery with Machine Learning and Cloud High-Performance Computing: from Large-Scale Screening to Experimental Validation142A review of inverse problems for generalized elastic media: formulations, experiments, synthesis64Physical studies of spintronics-based Heusler alloys63Pivotal Role of Surface Terminations in MXene Thermodynamic Stability83Generative Pretrained Transformer for Heterogeneous Catalysts51A hybrid modelling approach to compare chemical separation technologies in terms of energy consumption and carbon dioxide emissions65Cross-disciplinary perspectives on the potential for artificial intelligence across chemistry51Two-Dimensional Transition Metal Dichalcogenides: A Theory and Simulation Perspective84Inverse design of nanophotonic devices enabled by optimization algorithms and deep learning: recent achievements and future prospects56High‐Conductivity, Self‐Healing, and Adhesive Ionic Hydrogels for Health Monitoring and Human‐Machine Interactions Under Extreme Cold Conditions99AI-Driven Inverse Design of Materials: Past, Present, and Future78Machine Learning-Based Computational Design Methods for High-Entropy Alloys48Artificial Intelligence‐Driven Approaches in Semiconductor Research44Recent advances in machine learning-assisted design of additive manufacturing metastructures: a review54Machine Learning for Accelerating Energy Materials Discovery: Bridging Quantum Accuracy with Computational Efficiency43Artificial intelligence-driven approaches for materials design and discovery102Inverse design of glass structure with deep graph neural networks45

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