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QSAR without borders

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First Principles Neural Network Potentials for Reactive Simulations of Large Molecular and Condensed Systems

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A Critical Review of Machine Learning of Energy Materials

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Including crystal structure attributes in machine learning models of formation energies via Voronoi tessellations

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Machine Learning for Computational Heterogeneous Catalysis

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Perspectives on the Impact of Machine Learning, Deep Learning, and Artificial Intelligence on Materials, Processes, and Structures Engineering

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Machine learning in materials science: From explainable predictions to autonomous design

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Rational Design: A High-Throughput Computational Screening and Experimental Validation Methodology for Lead-Free and Emergent Hybrid Perovskites

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Machine Learning Meets with Metal Organic Frameworks for Gas Storage and Separation

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Identifying Pb-free perovskites for solar cells by machine learning

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Machine Learning Approaches for Thermoelectric Materials Research

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Machine learning in materials genome initiative: A review

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High-throughput density-functional perturbation theory phonons for inorganic materials

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

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Machine-learned interatomic potentials by active learning: amorphous and liquid hafnium dioxide

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Statistical Analysis of Coordination Environments in Oxides

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Artificial intelligence to bring nanomedicine to life

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Transformation towards smart factory system: Examining new job profiles and competencies

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Understanding the ionic conductivity maximum in doped ceria: trapping and blocking

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Machine Learning for Perovskite Solar Cells and Component Materials: Key Technologies and Prospects

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Accurate force field for molybdenum by machine learning large materials data

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Machine Learning in Materials Discovery: Confirmed Predictions and Their Underlying Approaches

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Overview on Theoretical Simulations of Lithium‐Ion Batteries and Their Application to Battery Separators

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Emerging artificial intelligence in piezoelectric and triboelectric nanogenerators

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Artificial intelligence-enabled smart mechanical metamaterials: advent and future trends

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Discovery of Intermetallic Compounds from Traditional to Machine-Learning Approaches

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Machine learning for material characterization with an application for predicting mechanical properties

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Comparison of approximations in density functional theory calculations: Energetics and structure of binary oxides

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A focused review on machine learning aided high-throughput methods in high entropy alloy

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Recent advances in knowledge discovery for heterogeneous catalysis using machine learning

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3‑D Inorganic Crystal Structure Generation and Property Prediction via Representation Learning

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Extending Shannon's ionic radii database using machine learning

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Machine Learning and Big Data Provide Crucial Insight for Future Biomaterials Discovery and Research

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How Machine Learning Predicts and Explains the Performance of Perovskite Solar Cells

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Assessing Local Structure Motifs Using Order Parameters for Motif Recognition, Interstitial Identification, and Diffusion Path Characterization

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Lattice dynamics simulation using machine learning interatomic potentials

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A Simple Computational Proxy for Screening Magnetocaloric Compounds

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A Universal 3D Voxel Descriptor for Solid-State Material Informatics with Deep Convolutional Neural Networks

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Embedding domain knowledge for machine learning of complex material systems

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The Materials Project: Accelerating Materials Design Through Theory-Driven Data and Tools

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New horizons in thermoelectric materials: Correlated electrons, organic transport, machine learning, and more

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Machine learning elastic constants of multi-component alloys

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Artificial intelligence for search and discovery of quantum materials

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Catalyze Materials Science with Machine Learning

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A computational assessment of the electronic, thermoelectric, and defect properties of bournonite (CuPbSbS 3 ) and related substitutions

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Artificial intelligence for photonics and photonic materials

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Using Deep Machine Learning to Understand the Physical Performance Bottlenecks in Novel Thin‐Film Solar Cells

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Machine learning for algal biofuels: a critical review and perspective for the future

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Materials informatics with machine learning

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Informatics is fueling new materials discovery

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