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Autonomous Intelligent Agents for Accelerated Materials Discovery

preprint
submitted on 17.02.2020 and posted on 19.02.2020 by Joseph H. Montoya, Kirsten Winther, Raul A. Flores, Thomas Bligaard, Jens Strabo Hummelshøj, Muratahan Aykol
We present an end-to-end computational system for autonomous materials discovery. The system aims for cost-effective optimization in large, high-dimensional search spaces of materials by adopting a sequential, agent-based approach to deciding which experiments to carry out. In choosing next experiments, agents can make use of past knowledge, surrogate models, logic, thermodynamic or other physical constructs, heuristic rules, and different exploration-exploitation strategies. We show a series of examples for (i) how the discovery campaigns for finding materials satisfying a relative stability objective can be simulated to design new agents, and (ii) how those agents can be deployed in real discovery campaigns to control experiments run externally, such as the cloud-based density functional theory simulations in this work. In a sample set of 16 campaigns covering a range of binary and ternary chemistries including metal oxides, phosphides, sulfides and alloys, this autonomous platform found 383 new stable or nearly stable materials with no intervention by the researchers.

History

Email Address of Submitting Author

murat.aykol@tri.global

Institution

Toyota Research Institute

Country

United States

ORCID For Submitting Author

0000-0001-6433-7217

Declaration of Conflict of Interest

M.A. has a related U.S. patent application. The remaining authors declare no conflict of interest.

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