Machine learning-guided design, synthesis, and characterization of atomically dispersed electrocatalysts

Li, Sirui; Zhang, Hanguang; Holby, Edward F.; Zelenay, Piotr; Kort-Kamp, Wilton Junior de Melo

doi:10.1016/j.coelec.2024.101578

Title: Machine learning-guided design, synthesis, and characterization of atomically dispersed electrocatalysts

Journal Article · Wed Aug 21 00:00:00 UTC 2024 · Current Opinion in Electrochemistry

DOI: https://doi.org/10.1016/j.coelec.2024.101578 · OSTI ID:2438514

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Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)

The recent integration of machine learning into materials design has revolutionized the understanding of structure–property relationships and optimization of material properties beyond the trial-and-error paradigm. On one hand, machine learning has significantly accelerated the development of atomically dispersed metal-nitrogen-carbon (M-N-C) electrocatalysts, which traditionally heavily relied on heuristic approaches. On the other hand, the primary challenge of leveraging machine learning to expedite M-N-C materials discovery lies in the cost associated with data collection. Here, we review recent machine learning integration strategies for M-N-C catalyst development, including discussions on the typical algorithms such as symbolic regression and convolutional neural networks employed for the theoretical design, synthesis optimization via active learning, and advanced microscopy characterization. Subsequently, we provide our perspective on potential near-future directions for furthering machine learning-assisted development of new M-N-C catalysts and elucidating the complex physicochemical mechanisms governing the selectivity, activity, and durability in this class of materials.

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Research Organization:: Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)

Sponsoring Organization:: USDOE; USDOE National Nuclear Security Administration (NNSA); USDOE Office of Energy Efficiency and Renewable Energy (EERE), Office of Sustainable Transportation. Hydrogen Fuel Cell Technologies Office (HFTO)

Grant/Contract Number:: 89233218CNA000001

OSTI ID:: 2438514

Report Number(s):: LA-UR--24-25196

Journal Information:: Current Opinion in Electrochemistry, Journal Name: Current Opinion in Electrochemistry Vol. 48; ISSN 2451-9103

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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