ML & Physics-Based Property Prediction and Iterative Self-Driving Lab
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.
Resources (10)
Computational Studies of PbS Quantum Dots
Research and Reviews
Machine learning enhanced evaluation of semiconductor quantum dots
Research and Reviews
Lila
Company
Will Stoy
Individual
Acceleration Consortium
Research Org
Darwin Superconductors by Will Stoy
Whitepapers and Essays
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.