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RESEARCH PAPER · Nature Materials · 2025

Accelerated data-driven materials science with the Materials Project

Matthew K. Horton, Patrick Huck, Ruoxi Yang, Jason M. Munro, Shyam Dwaraknath, Alex M. Ganose, Ryan Kingsbury, Mingjian Wen, Jimmy X. Shen, Tyler S. Mathis, Aaron D. Kaplan, Karlo Berket, Janosh Riebesell, Janine George, Andrew Rosen, Evan Walter Clark Spotte‐Smith, Matthew J. McDermott, Orion Cohen, Alexander Dunn, Matthew C. Kuner, Gian‐Marco Rignanese, Guido Petretto, David Waroquiers, Sinéad M. Griffin, Jeffrey B. Neaton, D. C. Chrzan, Mark Asta, Geoffroy Hautier, Shreyas Cholia, Gerbrand Ceder, Shyue Ping Ong, Anubhav Jain, Kristin Aslaug Persson

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Accelerated data-driven materials science with the Materials ProjectOriginalComputationally accelerated discovery of mixed metal compounds for chemical looping combustion and beyond7Evaluating the potential of CsBiSCl 2 as a solar absorber0Validation Authority Erosion in High-Velocity Computational Materials Innovation0Materials informatics: A review of AI and machine learning tools, platforms, data repositories, and applications to architectured porous materials41Toward High-Voltage Cathodes for Zinc-Ion Batteries: Discovery Pipeline and Material Design Rules6An automated framework for exploring and learning potential-energy surfaces24The strong-weak adsorption pair motif of cu Fe bimetallic composite as an efficient catalyst for CO2 RR toward the C2product ethylene and ethanol0Ethylene carbonate decomposition on pristine and oxygen-deficient binary metal oxide surfaces of battery cathode coating2Darwin4Matter: A Platform Integrating Machine Learning and Quantum Chemistry for New Materials Design0AI is dreaming up millions of new materials. Are they any good?8Accelerated Discovery of Cost-Effective Photoabsorber Materials for Near-Infrared (λ = 1600 nm) Photodetector Applications1Cross-functional transferability in foundation machine learning interatomic potentials12Electrochemical Stability and Reduction Mechanism of LLZO Interfaces in Li Batteries from First Principles7Bandgap prediction and design of halide double perovskites via ensemble machine learning6Atomistic Polymer Modeling: Recent Advances and Challenges in Building and Parametrization Workflows9Strategies to Suppress Thermal Conductivity in Half-Heusler Compounds for Thermoelectric Applications: A Comprehensive Review8Building an affordable self-driving lab: Practical machine learning experiments for physics education using Internet-of-Things1From Classical Ferroelectricity to Emerging Low‐Dimensional Phases4C'mon! 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Hg) for photovoltaic applications8A Self-Supervised Graph Attention Network for Accelerating the Discovery of High-Conductivity Solid-State Electrolytes0Product design, synthesis, and lab automation with The World Avatar0DFT validated machine learning model for elastic property prediction and mechanical classification of ternary cubic alloys0DCC: A Model-Free Frame to Evaluate Data Set Quality1SMC-X: A Distributed, Scalable Monte Carlo Simulation Method for Chemically Complex Alloys3Two-dimensional layered metal oxides (2D LMOs) for next-generation electronic devices8Lithiation-dependent solid electrolyte interphase nucleation pathways in Si/Li6PS5Cl interfaces uncovered by machine learning-accelerated simulations5A dataset of Curie and Néel temperatures auto-generated with ChemDataExtractor and the Snowball algorithm2Laser directed energy deposition of the refractory compositionally complex alloy TMT-8Cr–10Al: Processability, microstructural evolution and phase formation0Architecture 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