Machine intelligence accelerated design of conductive MXene aerogels with programmable properties

Snehi Shrestha, Kieran James Barvenik, Tianle Chen, Haochen Yang, Yang Li, Meera Muthachi Kesavan, Joshua M. Little, Hayden C. Whitley, Zi Teng, Yaguang Luo, Eleonora Tubaldi () and Po-Yen Chen ()
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Snehi Shrestha: University of Maryland
Kieran James Barvenik: University of Maryland
Tianle Chen: University of Maryland
Haochen Yang: University of Maryland
Yang Li: University of Maryland
Meera Muthachi Kesavan: University of Maryland
Joshua M. Little: University of Maryland
Hayden C. Whitley: University of Maryland
Zi Teng: Beltsville Agricultural Research Center
Yaguang Luo: Beltsville Agricultural Research Center
Eleonora Tubaldi: University of Maryland
Po-Yen Chen: University of Maryland

Nature Communications, 2024, vol. 15, issue 1, 1-14

Abstract: Abstract Designing ultralight conductive aerogels with tailored electrical and mechanical properties is critical for various applications. Conventional approaches rely on iterative, time-consuming experiments across a vast parameter space. Herein, an integrated workflow is developed to combine collaborative robotics with machine learning to accelerate the design of conductive aerogels with programmable properties. An automated pipetting robot is operated to prepare 264 mixtures of Ti3C2Tx MXene, cellulose, gelatin, and glutaraldehyde at different ratios/loadings. After freeze-drying, the aerogels’ structural integrity is evaluated to train a support vector machine classifier. Through 8 active learning cycles with data augmentation, 162 unique conductive aerogels are fabricated/characterized via robotics-automated platforms, enabling the construction of an artificial neural network prediction model. The prediction model conducts two-way design tasks: (1) predicting the aerogels’ physicochemical properties from fabrication parameters and (2) automating the inverse design of aerogels for specific property requirements. The combined use of model interpretation and finite element simulations validates a pronounced correlation between aerogel density and compressive strength. The model-suggested aerogels with high conductivity, customized strength, and pressure insensitivity allow for compression-stable Joule heating for wearable thermal management.

Date: 2024
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DOI: 10.1038/s41467-024-49011-8

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