A framework for fully autonomous design of materials via multiobjective optimization and active learning: challenges and next steps
This work addresses the problem of efficient and autonomous materials discovery for researchers in chemistry and materials science, though it is incremental as it builds on existing optimization and active learning methods.
The authors tackled the challenge of autonomous materials design in costly, multiobjective experimental settings by developing an active learning workflow that integrates multiobjective optimization with continuously updated machine learning models, demonstrating it in a continuous-flow chemistry lab to identify optimal manufacturing conditions for an electrolyte.
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.