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

Abstract

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.