A scientific machine learning framework to understand flash graphene synthesis

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dc.citation.firstpage1209
dc.citation.journalTitleDigital Discovery
dc.citation.lastpage1218
dc.citation.volumeNumber2
dc.contributor.authorSattari, Kianoosh
dc.contributor.authorEddy, Lucas
dc.contributor.authorBeckham, Jacob L.
dc.contributor.authorWyss, Kevin M.
dc.contributor.authorByfield, Richard
dc.contributor.authorQian, Long
dc.contributor.authorTour, James M.
dc.contributor.authorLin, Jian
dc.contributor.orgNanoCarbon Center
dc.contributor.orgWelch Institute for Advanced Materials
dc.date.accessioned2024-05-08T18:56:13Z
dc.date.available2024-05-08T18:56:13Z
dc.date.issued2023
dc.description.abstractFlash Joule heating (FJH) is a far-from-equilibrium (FFE) processing method for converting low-value carbon-based materials to flash graphene (FG). Despite its promises in scalability and performance, attempts to explore the reaction mechanism have been limited due to the complexities involved in the FFE process. Data-driven machine learning (ML) models effectively account for the complexities, but the model training requires a considerable amount of experimental data. To tackle this challenge, we constructed a scientific ML (SML) framework trained by using both direct processing variables and indirect, physics-informed variables to predict the FG yield. The indirect variables include current-derived features (final current, maximum current, and charge density) predicted from the proxy ML models and reaction temperatures simulated from multi-physics modeling. With the combined indirect features, the final ML model achieves an average R2 score of 0.81 ± 0.05 and an average RMSE of 12.1% ± 2.0% in predicting the FG yield, which is significantly higher than the model trained without them (R2 of 0.73 ± 0.05 and an RMSE of 14.3% ± 2.0%). Feature importance analysis validates the key roles of these indirect features in determining the reaction outcome. These results illustrate the promise of this SML to elucidate FFE material synthesis outcomes, thus paving a new avenue to processing other datasets from the materials systems involving the same or different FFE processes.
dc.identifier.citationSattari, K., Eddy, L., L. Beckham, J., M. Wyss, K., Byfield, R., Qian, L., M. Tour, J., & Lin, J. (2023). A scientific machine learning framework to understand flash graphene synthesis. Digital Discovery, 2(4), 1209–1218. https://doi.org/10.1039/D3DD00055A
dc.identifier.digitald3dd00055a
dc.identifier.doihttps://doi.org/10.1039/D3DD00055A
dc.identifier.urihttps://hdl.handle.net/1911/115697
dc.language.isoeng
dc.publisherRoyal Society of Chemistry
dc.rightsExcept where otherwise noted, this work is licensed under a Creative Commons Attribution-NonCommercial (CC BY-NC) license. Permission to reuse, publish, or reproduce the work beyond the terms of the license or beyond the bounds of fair use or other exemptions to copyright law must be obtained from the copyright holder.
dc.rights.urihttps://creativecommons.org/licenses/by-nc/3.0/
dc.titleA scientific machine learning framework to understand flash graphene synthesis
dc.typeJournal article
dc.type.dcmiText
dc.type.publicationpublisher version
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