Machine Learning Guided Synthesis of Flash Graphene

Beckham, Jacob L.; Wyss, Kevin M.; Xie, Yunchao; McHugh, Emily A.; Li, John Tianci; Advincula, Paul A.; Chen, Weiyin; Lin, Jian; Tour, James M.

doi:10.1002/adma.202106506

Title: Machine Learning Guided Synthesis of Flash Graphene

Journal Article · 21 January 2022 · Advanced Materials

DOI: https://doi.org/10.1002/adma.202106506 · OSTI ID:1881497

Beckham, Jacob L. ^[1]; Wyss, Kevin M. ^[2]; Xie, Yunchao ^[3]; McHugh, Emily A. ^[2]; Li, John Tianci ^[2]; Advincula, Paul A. ^[2]; Chen, Weiyin ^[2]; Lin, Jian ^[3];

^[2]

Rice Univ., Houston, TX (United States); Rice University
Rice Univ., Houston, TX (United States)
Univ. of Missouri, Columbia, MO (United States)

Advances in nanoscience have enabled the synthesis of nanomaterials, such as graphene, from low-value or waste materials through flash Joule heating. Though this capability is promising, the complex and entangled variables that govern nanocrystal formation in the Joule heating process remain poorly understood. In this work, machine learning (ML) models are constructed to explore the factors that drive the transformation of amorphous carbon into graphene nanocrystals during flash Joule heating. An XGBoost regression model of crystallinity achieves an r² score of 0.8051 ± 0.054. Feature importance assays and decision trees extracted from these models reveal key considerations in the selection of starting materials and the role of stochastic current fluctuations in flash Joule heating synthesis. Furthermore, partial dependence analyses demonstrate the importance of charge and current density as predictors of crystallinity, implying a progression from reaction-limited to diffusion-limited kinetics as flash Joule heating parameters change. Finally, a practical application of the ML models is shown by using Bayesian meta-learning algorithms to automatically improve bulk crystallinity over many Joule heating reactions. Furthermore, these results illustrate the power of ML as a tool to analyze complex nanomanufacturing processes and enable the synthesis of 2D crystals with desirable properties by flash Joule heating.

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Research Organization:: Rice Univ., Houston, TX (United States)

Sponsoring Organization:: USDOE

Grant/Contract Number:: FE0031794; FE0031645

OSTI ID:: 1881497

Journal Information:: Advanced Materials, Journal Name: Advanced Materials Journal Issue: 12 Vol. 34; ISSN 0935-9648

Publisher:: WileyCopyright Statement

Country of Publication:: United States

Language:: English

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