Accelerated data-driven materials science with the Materials Project

2025/07/03 by Matthew K. Horton, Patrick Huck, Ruo Xi Yang +38 · 1 voice · 1 citation
Engineering · Materials Science · #Advanced Materials Characterization Techniques #Machine Learning in Materials Science #X-ray Diffraction in Crystallography

paper · doi:10.1038/s41563-025-02272-0

crossref issued 2025/07/03 · crossref published 2025/07/03 · crossref published-online 2025/07/03 · openalex publication_date 2025/07/03 · crossref created 2025/07/03 · crossref deposited 2025/09/30 · crossref published-print 2025/10/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01 · crossref indexed 2026/08/01

Citations

• From ultrasoft pseudopotentials to the projector augmented-wave method
• Efficient iterative schemes forab initiototal-energy calculations using a plane-wave basis set
• Crystallography Open Database – an open-access collection of crystal structures
• The AFLOW Standard for High-Throughput Materials Science Calculations
• Accurate and Numerically Efficient r2SCAN Meta-Generalized Gradient Approximation
• A universal graph deep learning interatomic potential for the periodic table
• A database of molecular properties integrated in the Materials Project
• An autonomous laboratory for the accelerated synthesis of inorganic materials
• Scaling deep learning for materials discovery
• Atomate2: Modular workflows for materials science

Cited by

• MP-ALOE: An r2SCAN dataset for universal machine learning interatomic potentials
• exa-AMD: A Scalable Workflow for Accelerating AI-Assisted Materials Discovery and Design

Discussions

• With ~180K materials and millions of calculated properties, the Materials Project enables inverse design, synthesis screening, and discovery. Examples include phosphors, thermoelectrics, electrides, a [bsky, 0 points, 0 comments]