A Surrogate Machine Learning Model for the Design of Single-Atom Catalyst on Carbon and Porphyrin Supports towards Electrochemistry

We apply the machine learning (ML) tool to calculate
the Gibbs
free energy (ΔG) of reaction intermediates
rapidly and accurately as a guide for designing porphyrin- and graphene-supported
single-atom catalysts (SACs) toward electrochemical reactions. Based
on the 2105 DFT calculation data from the literature, we trained a
support vector machine (SVR) algorithm. The hyperparameters were optimized
using Bayesian optimization along with 10-fold cross-validation to
avoid overfitting. Based on the Shapley Additive exPlanation (SHAP)
and permutation methods, the feature importance analysis suggests
that the most important parameters are the number of pyridinic nitrogen
(Npy), the number of d electrons (θd), and the number
of valence electrons of reaction intermediates. Inspired by this feature
importance analysis and the Pearson correlation coefficient, we found
a linear dependent, simple, and general descriptor (φ) to describe
ΔG of reaction intermediates (e.g., ΔGOH* = 0.020φ –
2.190). Using the trained SVR algorithm, ΔGOH*, ΔGO*, ΔGOOH*, ΔGOO*, ΔGH*, ΔGCOOH*, ΔGCO*, and ΔGN2* intermediates are predicted for the oxygen
reduction reaction (ORR), the oxygen evolution reaction (OER), the
hydrogen evolution reaction (HER), and the CO2 reduction
reaction (CO2RR). The SVR model predicts an ORR overpotential
of 0.51 V and an HER overpotential of 0.22 V for FeN4-SAC. Moreover,
we used the SVR algorithm for high-throughput screening of SACs, suggesting
new SACs with low ORR overpotentials. This strategy provides a data-driven
catalyst design method that significantly reduces the costs of DFT
calculations while providing the means for designing SACs for electrocatalysis
and beyond.