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.
@article{arxiv.2304.07445,
title = {A framework for fully autonomous design of materials via multiobjective optimization and active learning: challenges and next steps},
author = {Tyler H. Chang and Jakob R. Elias and Stefan M. Wild and Santanu Chaudhuri and Joseph A. Libera},
journal= {arXiv preprint arXiv:2304.07445},
year = {2023}
}