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

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. Finally,
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.