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

• URL: http://arxiv.org/abs/2304.07445v1
• Date: Sat, 15 Apr 2023 01:34:16 GMT
• Title: A framework for fully autonomous design of materials via multiobjective optimization and active learning: challenges and next steps
• Authors: Tyler H. Chang and Jakob R. Elias and Stefan M. Wild and Santanu Chaudhuri and Joseph A. Libera
• Abstract summary: 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.
• Score: 2.6047112351202784
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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