Machine learning-accelerated electrocatalytic microkinetic model for global reaction network simulation of CO2 reduction reaction

doi:10.1016/S1872-2067(26)65208-4

Abstract

Abstract:

Electrochemical CO₂ reduction represents a sustainable route for energy storage and carbon utilization. Despite copper-based catalysts uniquely enabling hydrocarbons and oxygenates product formation, advancing this technology requires deeper mechanistic insights and accelerated catalyst screening. Herein, we establish a novel electrocatalysis research framework integrating three synergistic components: (1) With a force convergence accuracy of 0.02 eV/Å, the AdsorbML-FT workflow can achieve a 24-fold acceleration in DFT optimization steps whilst accounting for adsorption site screening, thereby enabling more efficient energy calculations for general substrate-adsorbate systems; (2) the global reaction network simulation of CO₂RR enabling broader coverage of reaction mechanism including 22 surface species and 40 elementary reactions beyond traditional optimal-path limitations; (3) the electrocatalytic microkinetic model ensuring greater accuracy in predicting both catalytic activity and product selectivity. This integrated approach quantitatively decodes the CO₂-to-C₁ mechanism on Cu(111) and systematically screens 37 Cu₃M₁ alloys. We identify Cu₃Al₁ as an optimal catalyst, driving CO conversion at relatively low overpotentials with simultaneously high catalytic activity and CH₄ product selectivity. The proposed machine learning-accelerated electrocatalytic microkinetic model bridges theoretical simulations with experimental measurements, offering a transformative tool for rapid electrocatalyst discovery.

Key words: CO₂ reduction reaction, Machine learning, Density functional theory, Microkinetic model, Reaction mechanism

Xiao-Li Liang, Zhao-Yu Wang, Shi-Lin Yan, Ting-Ting Yin, Wei Zhang, Hao Zhang, Xiaohong Liu, Liu-Bin Zhao. Machine learning-accelerated electrocatalytic microkinetic model for global reaction network simulation of CO₂ reduction reaction[J]. Chinese Journal of Catalysis, 2026, 91: 163-175.

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URL: https://www.cjcatal.com/EN/10.1016/S1872-2067(26)65208-4

https://www.cjcatal.com/EN/Y2026/V91/I12/163

Figures/Tables 6

Fig. 1. Workflow of ML accelerated DFT calculations by AdsorbML-FT: (1) Initial configuration sampling: initial “adsorbate + substrate” configurations are generated for different combinations of adsorbate and substrate. (2) ML pre-screening: all initial configurations undergo ML-driven relaxation yielding ML-pre-relaxed configurations. (3) 3-Step DFT calculation: A three-step consecutive relaxation cycle is performed on the pre-relaxed configurations to calculate DFT single-point (DFT(SP)), which are used for validation or subsequent fine-tuning. (4) Fine tuning training: Use DFT single-point data for fine-tuning the pre-trained model. (5) Refined ML prediction: The fine-tuned ML (FT) model reprocesses different combinations of adsorbate and substrate to obtain a new set of more accurate ML-pre-relaxed optimal configurations. (6) Final DFT relaxation: The optimal configuration determined by ML(FT) will undergo a full DFT relaxation.

Fig. 2. (a) Top view of the Cu3Al1(111) surface. (b) DFT relaxation and adsorption energy changes of initial structures obtained by three methods (Heuristic, ML, ML(FT)), where the “SCF step” corresponds to the self-consistent field (SCF) calculation step in DFT. (c) Adsorption potential energy surface of CO on the surface shown in (a). (d) Adsorption energy profile along the x=5.3 Å line in (c), predicted by three different models，where ML(FT)-15 denotes a machine learning model trained using 15 DFT data points, and ML(FT)-503 denotes a machine learning model trained using 503 DFT data points.

Fig. 3. (a) Comparison of adsorption energies between pre-trained ML predictions and DFT(SP) on the validation set. (b) Validation set performance: Fine-tuned ML model (537 training points) vs. DFT(SP) adsorption energies. (c) Reproducibility test: Violin plot showing the distribution of R2 scores for adsorption energies on independently constructed DFT validation (three-step single-point calculations per adsorbate + substrate combination) points over 50 independent runs. (d) Assessment of initial structure quality: Absolute deviation (ΔEads) between DFT single-point energies of ML-predicted initial structures and final relaxed states across 48 systems.

Fig. 4. (a) Global reaction networks of CO2 reduction to C1 products. (b) Partial and total reaction rates as a function of electrode potential evaluated by GRN. (c) Faradaic efficiency as a function of electrode potential calculated by GRN. (d) and (e) Comparison of catalytic activities and product selectivities estimated by GRN and ORP.

Fig. 5. Total reaction rates (a) and activation barriers (b) of CORR on Cu3M1 catalysts at U=-1.0 V vs. SHE. (c,d) Proportional function fitting of total reaction rate with rate constant of rate-determining step at U=-0.3 and -1.0 V vs. SHE. (e) Calculated coefficients of determination as a function of electrode potential.

Fig. 6. (a,b) Product selectivity on different substrates with U=-0.3 and -1.0 V vs. SHE. Faradaic efficiency of CORR as a function of electrode potential on Cu3Au1 (c), Cu3In1 (d), and Cu3Al1 (e).

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Machine learning-accelerated electrocatalytic microkinetic model for global reaction network simulation of CO₂ reduction reaction

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