Interpretable Machine Learning Models for Practical Antimonate Electrocatalyst Performance.

Autor: Deo S; Department of Chemical Engineering, Stanford University, Stanford, CA 94305, United States.; SUNCAT Center for Interface Science and Catalysis, SLAC National Accelerator Laboratory, Menlo Park, CA 94025, United States., Kreider ME; Department of Chemical Engineering, Stanford University, Stanford, CA 94305, United States.; SUNCAT Center for Interface Science and Catalysis, SLAC National Accelerator Laboratory, Menlo Park, CA 94025, United States., Kamat G; Department of Chemical Engineering, Stanford University, Stanford, CA 94305, United States.; SUNCAT Center for Interface Science and Catalysis, SLAC National Accelerator Laboratory, Menlo Park, CA 94025, United States., Hubert M; Department of Chemical Engineering, Stanford University, Stanford, CA 94305, United States.; SUNCAT Center for Interface Science and Catalysis, SLAC National Accelerator Laboratory, Menlo Park, CA 94025, United States., Zamora Zeledón JA; Department of Chemical Engineering, Stanford University, Stanford, CA 94305, United States.; SUNCAT Center for Interface Science and Catalysis, SLAC National Accelerator Laboratory, Menlo Park, CA 94025, United States., Wei L; Department of Chemical Engineering, Stanford University, Stanford, CA 94305, United States.; SUNCAT Center for Interface Science and Catalysis, SLAC National Accelerator Laboratory, Menlo Park, CA 94025, United States., Matthews J; Department of Chemical Engineering, Stanford University, Stanford, CA 94305, United States.; SUNCAT Center for Interface Science and Catalysis, SLAC National Accelerator Laboratory, Menlo Park, CA 94025, United States., Keyes N; Department of Chemical Engineering, Stanford University, Stanford, CA 94305, United States.; SUNCAT Center for Interface Science and Catalysis, SLAC National Accelerator Laboratory, Menlo Park, CA 94025, United States., Singh I; Department of Chemical Engineering, Stanford University, Stanford, CA 94305, United States.; SUNCAT Center for Interface Science and Catalysis, SLAC National Accelerator Laboratory, Menlo Park, CA 94025, United States., Jaramillo TF; Department of Chemical Engineering, Stanford University, Stanford, CA 94305, United States.; SUNCAT Center for Interface Science and Catalysis, SLAC National Accelerator Laboratory, Menlo Park, CA 94025, United States., Abild-Pedersen F; SUNCAT Center for Interface Science and Catalysis, SLAC National Accelerator Laboratory, Menlo Park, CA 94025, United States., Burke Stevens M; SUNCAT Center for Interface Science and Catalysis, SLAC National Accelerator Laboratory, Menlo Park, CA 94025, United States., Winther K; SUNCAT Center for Interface Science and Catalysis, SLAC National Accelerator Laboratory, Menlo Park, CA 94025, United States., Voss J; SUNCAT Center for Interface Science and Catalysis, SLAC National Accelerator Laboratory, Menlo Park, CA 94025, United States.
Jazyk: angličtina
Zdroj: Chemphyschem : a European journal of chemical physics and physical chemistry [Chemphyschem] 2024 Jul 02; Vol. 25 (13), pp. e202400010. Date of Electronic Publication: 2024 Apr 25.
DOI: 10.1002/cphc.202400010
Abstrakt: Computationally predicting the performance of catalysts under reaction conditions is a challenging task due to the complexity of catalytic surfaces and their evolution in situ, different reaction paths, and the presence of solid-liquid interfaces in the case of electrochemistry. We demonstrate here how relatively simple machine learning models can be found that enable prediction of experimentally observed onset potentials. Inputs to our model are comprised of data from the oxygen reduction reaction on non-precious transition-metal antimony oxide nanoparticulate catalysts with a combination of experimental conditions and computationally affordable bulk atomic and electronic structural descriptors from density functional theory simulations. From human-interpretable genetic programming models, we identify key experimental descriptors and key supplemental bulk electronic and atomic structural descriptors that govern trends in onset potentials for these oxides and deduce how these descriptors should be tuned to increase onset potentials. We finally validate these machine learning predictions by experimentally confirming that scandium as a dopant in nickel antimony oxide leads to a desired onset potential increase. Macroscopic experimental factors are found to be crucially important descriptors to be considered for models of catalytic performance, highlighting the important role machine learning can play here even in the presence of small datasets.
(© 2024 Wiley-VCH GmbH.)
Databáze: MEDLINE
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