384. A framework for fully autonomous design of materials via multiobjective optimization and active learning: challenges and next steps

Tyler H. Chang, Jakob R. Elias, Stefan M. Wild, Santanu Chaudhuri and Joseph A. Libera; arXiv (2023); Link: arxiv.org/pdf/2304.07445.pdf

In order to deploy machine learning in a real-world self-driving laboratory where data acquisition is costly and there are multiple competing design criteria, systems need to be able to intelligently sample while balancing performance trade-offs and constraints. For these reasons, we present an active learning process based on multiobjective black-box optimization with continuously updated machine learning models. This workflow is built on open-source technologies for real-time data streaming and modular multiobjective optimization software development. We demonstrate a proof of concept for this workflow through the autonomous operation of a continuous-flow chemistry laboratory, which identifies ideal manufacturing conditions for the electrolyte 2,2,2-trifluoroethyl methyl carbonate.