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DOE OSTI · 1959579

Machine Intelligence-Centered System for Automated Characterization of Functional Materials and Interfaces

Muckley, Eric S.·Vasudevan, Rama K.·Sumpter, Bobby G.·Advincula, Rigoberto C.·Ivanov, Ilia N.
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Abstract

Classic design of experiment relies on a time-intensive workflow that requires planning, data interpretation, and hypothesis building by experienced researchers. Here, in this paper, we describe an integrated, machine-intelligent experimental system which enables simultaneous dynamic tests of electrical, optical, gravimetric, and viscoelastic properties of materials under a programmable dynamic environment. Specially designed software controls the experiment and performs on-the-fly extensive data analysis and dynamic modeling, real-time iterative feedback for dynamic control of experimental conditions, and rapid visualization of experimental results. The system operates with minimal human intervention and enables time-efficient characterization of complex dynamic multifunctional environmental responses of materials with simultaneous data processing and analytics. The system provides a viable platform for artificial intelligence (AI)-centered material characterization, which, when coupled with an AI-controlled synthesis system, could lead to accelerated discovery of multifunctional materials.

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37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRYautonomous experimentconjugated polymerselectrical propertiesimpedancemachine learningmaterialsquartz crystal microbalanceresonance structuressensorssoftwarethin-film characterization

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Muckley, Eric S., Vasudevan, Rama K., Sumpter, Bobby G., Advincula, Rigoberto C., Ivanov, Ilia N.. 2022-12-28. Machine Intelligence-Centered System for Automated Characterization of Functional Materials and Interfaces. https://doi.org/10.1021/acsami.2c16088

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