Materials Property Bank
Large open dataset of experimentally determined mechanical, thermal, electrical properties of millions of samples that consolidates published and crowdsourced data to enable ML models.
This would augment initiatives like the Materials Project and OQMD, which are simulation-heavy.
Resources (2)
Open Catalyst Experiments 2024 (OCx24)
Initiative
Scaling deep learning for materials discovery
Technology Seed
R&D Gaps (1)
“New materials create fundamentally new human capabilities. And yet…new materials-enabled human capabilities have been rare in the past 50 years.” The core challenge lies in our inability to reliably design and manufacture materials that meet specific engineering requirements–and to do so at an industrial scale and reasonable cost.
Identifying promising new materials is hampered by the slow pace of exploration. The integration of machine learning, physics-based property prediction, and self-driving laboratories could dramatically accelerate this process. A significant opportunity lies in modeling the vast, unexplored space of potential materials in silico.