(18e) Camd: An AI-Accelerated Iterative Materials Discovery Platform

We present an end-to-end system for computational autonomy in materials discovery (CAMD). This system is designed to sequentially augment a given dataset according to user-specified strategies for choosing new experiments and experimental procedures. The decision-making entities, called "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 how discovery campaigns for finding materials satisfying a phase stability objective can be simulated to design new agents, and 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. Lastly, we present results from the cloud-based deployment of our tool that have resulted in over 2000 new inorganic crystalline materials in various crystal symmetries and compositions predicted to be within 200 meV of the phase diagram's convex hull.