Learning heterogeneous reaction kinetics from X-ray videos pixel by pixel

Hongbo Zhao, Haitao Dean Deng, Alexander E. Cohen, Jongwoo Lim, Yiyang Li, Dimitrios Fraggedakis, Benben Jiang, Brian D. Storey, William C. Chueh, Richard D. Braatz and Martin Z. Bazant ()
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Hongbo Zhao: Massachusetts Institute of Technology
Haitao Dean Deng: Stanford University
Alexander E. Cohen: Massachusetts Institute of Technology
Jongwoo Lim: Stanford University
Yiyang Li: Stanford University
Dimitrios Fraggedakis: Massachusetts Institute of Technology
Benben Jiang: Massachusetts Institute of Technology
Brian D. Storey: Toyota Research Institute
William C. Chueh: Stanford University
Richard D. Braatz: Massachusetts Institute of Technology
Martin Z. Bazant: Massachusetts Institute of Technology

Nature, 2023, vol. 621, issue 7978, 289-294

Abstract: Abstract Reaction rates at spatially heterogeneous, unstable interfaces are notoriously difficult to quantify, yet are essential in engineering many chemical systems, such as batteries1 and electrocatalysts2. Experimental characterizations of such materials by operando microscopy produce rich image datasets3–6, but data-driven methods to learn physics from these images are still lacking because of the complex coupling of reaction kinetics, surface chemistry and phase separation7. Here we show that heterogeneous reaction kinetics can be learned from in situ scanning transmission X-ray microscopy (STXM) images of carbon-coated lithium iron phosphate (LFP) nanoparticles. Combining a large dataset of STXM images with a thermodynamically consistent electrochemical phase-field model, partial differential equation (PDE)-constrained optimization and uncertainty quantification, we extract the free-energy landscape and reaction kinetics and verify their consistency with theoretical models. We also simultaneously learn the spatial heterogeneity of the reaction rate, which closely matches the carbon-coating thickness profiles obtained through Auger electron microscopy (AEM). Across 180,000 image pixels, the mean discrepancy with the learned model is remarkably small (

Date: 2023
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DOI: 10.1038/s41586-023-06393-x

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