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

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 acti...

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Bibliographic Details
Published in:arXiv.org 2023-04
Main Authors: Chang, Tyler H, Elias, Jakob R, Wild, Stefan M, Chaudhuri, Santanu, Libera, Joseph A
Format: Article
Language:English
Subjects:
Continuous flow
Data acquisition
Data transmission
Design criteria
Design optimization
Machine learning
Multiple objective analysis
Software development
Workflow
Online Access:Get full text
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Summary: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.
ISSN:2331-8422