Searching Through the Vast, Underexplored Space of Materials is Slow and Expensive
“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.
Foundational Capabilities (4)
Leverage machine learning models combined with physics-based property prediction to iteratively explore the materials space using automated, self-driving laboratory platforms, to find things like higher temperature superconductors or topological materials.
New designs are needed to minimize large capital expenditures and integrate flexible, modular components that can be rapidly repurposed for new experiments and are robust to variations and error handling.
Develop more efficient assay platforms to test the properties of materials, thus enabling faster feedback and iteration in materials discovery.
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.