Machine Learning Accelerated First-Principles Study of the Hydrodeoxygenation of Propanoic Acid

Yang, Wenqiang; Abdelfatah, Kareem E.; Kundu, Subrata Kumar; Rajbanshi, Biplab; Terejanu, Gabriel A.; Heyden, Andreas

doi:10.1021/acscatal.4c01419

Title: Machine Learning Accelerated First-Principles Study of the Hydrodeoxygenation of Propanoic Acid

Journal Article · Thu Jun 20 00:00:00 UTC 2024 · ACS Catalysis

DOI: https://doi.org/10.1021/acscatal.4c01419 · OSTI ID:3001213

^[1]; Abdelfatah, Kareem E. ^[1];

^[1]; Rajbanshi, Biplab ^[2]; Terejanu, Gabriel A. ^[3];

^[1]

University of South Carolina, Columbia, SC (United States)
University of South Carolina, Columbia, SC (United States); Visva-Bharati University, Santiniketan (India)
University of North Carolina at Charlotte, NC (United States)

The complex reaction network of catalytic biomass conversions often involves hundreds of surface intermediates and thousands of reaction steps, greatly hindering the rational design of metal catalysts for these conversions. Here, we present a framework of machine learning (ML)-accelerated first-principles studies for the hydrodeoxygenation (HDO) of propanoic acid over transition metal surfaces. The microkinetic model (MKM) is initially parametrized by ML-predicted energies and iteratively improved by identifying the rate-determining species and steps (RDS), computing their energies by density functional theory (DFT), and reparameterizing the MKM until all the RDS are computed by DFT. The Gaussian process (GP) model performs significantly better than the linear ridge regression model for predicting both the adsorption free energies and transition state free energies. Parameterized with energies from the GP model, only 5–20% of the full reaction network has to be computed by DFT for the MKM to possess DFT-level accuracy for the TOF and dominant reaction pathway. While the linear ridge regression model performs worse than the GP model, its performance is greatly improved when only transition states are predicted by the regression model and adsorption energies are computed by DFT. Overall, we find that a high accuracy in adsorption free energies is more important for a reliable MKM than a high accuracy in TS free energies. Lastly, based on the GP model with GOH and GCHCHCO as catalyst descriptors, we build two-dimensional volcano plots in activity and selectivity that can help design promising alloy catalysts for HDO reactions of organic acids.

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Research Organization:: University of South Carolina, Columbia, SC (United States)

Sponsoring Organization:: National Science Foundation (NSF); USDOE Office of Science (SC), Basic Energy Sciences (BES); XSEDE

Grant/Contract Number:: SC0007167

OSTI ID:: 3001213

Journal Information:: ACS Catalysis, Journal Name: ACS Catalysis Journal Issue: 13 Vol. 14; ISSN 2155-5435

Publisher:: American Chemical Society (ACS)Copyright Statement

Country of Publication:: United States

Language:: English

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Title: Machine Learning Accelerated First-Principles Study of the Hydrodeoxygenation of Propanoic Acid

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