AutoMat: Automated materials discovery for electrochemical systems
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43577_2022_424_ReferencePDF.pdf
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Author(s) • • • • • • • • •
Annevelink, Emil
Kurchin, Rachel
Muckley, Eric
Kavalsky, Lance
Hegde, Vinay I.
Sulzer, Valentin
Zhu, Shang
Pu, Jiankun
Farina, David
Johnson, Matthew
Date Issued
December 22, 2022
Publisher
Springer International Publishing
Citation
Annevelink, Emil, Kurchin, Rachel, Muckley, Eric, Kavalsky, Lance, Hegde, Vinay I. et al. 2022. "AutoMat: Automated materials discovery for electrochemical systems."
Version
Author's final manuscript
Abstract
Abstract
Large-scale electrification is vital to addressing the climate crisis, but several scientific and technological challenges remain to fully electrify both the chemical industry and transportation. In both of these areas, new electrochemical materials will be critical, but their development currently relies heavily on human-time-intensive experimental trial and error and computationally expensive first-principles, mesoscale, and continuum simulations. We present an automated workflow, AutoMat, which accelerates these computational steps by introducing both automated input generation and management of simulations across scales from first principles to continuum device modeling. Furthermore, we show how to seamlessly integrate multi-fidelity predictions, such as machine learning surrogates or automated robotic experiments “in-the-loop.” The automated framework is implemented with design space search techniques to dramatically accelerate the overall materials discovery pipeline by implicitly learning design features that optimize device performance across several metrics. We discuss the benefits of AutoMat using examples in electrocatalysis and energy storage and highlight lessons learned.
Graphical abstract
MIT Department
Massachusetts Institute of Technology. Department of Mathematics
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1557/s43577-022-00424-0