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

Eric S. Muckley; Rama Vasudevan; Bobby G. Sumpter; Rigoberto C. Advincula; Ilia N. Ivanov

doi:10.1021/acsami.2c16088

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

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

Research output: Contribution to journal › Article › peer-review

3 Scopus citations

Abstract

Classic design of experiment relies on a time-intensive workflow that requires planning, data interpretation, and hypothesis building by experienced researchers. Here, 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.

Original language	English
Pages (from-to)	2329-2340
Number of pages	12
Journal	ACS Applied Materials and Interfaces
Volume	15
Issue number	1
DOIs	https://doi.org/10.1021/acsami.2c16088
State	Published - Jan 11 2023

Funding

This research was conducted at the Center for Nanophase Materials Sciences, a US Department of Energy Office of Science User Facility, operated at Oak Ridge National Laboratory. The research used resources of the Compute and Data Environment for Science (CADES) at the Oak Ridge National Laboratory, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC05-00OR22725. The authors thank Michal Pelach for his design of environmental flow cells. The development of the automated workflow and the autoML was supported by the INTERSECT Initiative as part of the Directed Research and Development Program of Oak Ridge National Laboratory, managed by UT-Battelle, LLC, for the US Department of Energy under contract DE-AC05-00OR22725.

Funders	Funder number
CADES
Data Environment for Science
U.S. Department of Energy	DE-AC05-00OR22725
Office of Science
Oak Ridge National Laboratory

Keywords

autonomous experiment
impedance
machine learning
quartz crystal microbalance
sensors
thin-film characterization

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10.1021/acsami.2c16088

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

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Keywords

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