Insights into pH-driven CO2 reduction through machine learning

Some of the most encouraging results of reaction-enhancing catalysts come from one material, tin (SN). The overall usefulness of SN as a catalyst is well known, but the underlying structure-performance relationship is not well understood, limiting its ability to maximize its potential. To address this knowledge gap, researchers at Tohoku University's Advanced Materials Research Institute (WPI-AIMR) used machine learning to characterize SN catalytic activity. A highly accurate simulation can be a game changer that helps researchers quickly and quickly design complex catalysts with high performance.

“The reason these catalysts are so important is that they can convert harmful carbon dioxide – CO₂ – Carbon-based fuels using renewable electricity provide sustainable solutions to energy shortages and climate change, explains Hao Li of WPI-IAMR.

Machine Learning Potential (MLP) Training for Surface Reconstruction Analysis. (a) Workflows for MLP training and large-scale configuration space search. (BC) Molecular Dynamics (MD) Simulation on the Nanosecond Scale (b) Mesoscopic Size of SNOs_{2 x} (c)SNS_{2 x}. The inset shows structures that may have the lowest energy surface screened by large-scale sampling. Bright gray, red and yellow spheres represent Sn, O, and S, respectively. ©Hao Li et al.

To closely examine SN catalysts, they employ machine learning possibilities to perform large-scale molecular dynamics simulations and successfully capture the reconstructed configuration of SNOs₂/sns₂. This approach used data from over 1,000 experimental literature sources to identify a variety of SN-based catalysts.

“Instead of spending days, months, or even years of these experiments rather than doing them in the lab, you can run these sophisticated, data-driven simulations. These simulations can help you find the attention to which lab-based experiments are attracting attention,” says Li.

pH-dependent microkinetic modeling on the RHE scale. (a) pH-dependent microkinetic modeling of co₂RR for SN-based catalysts. A lower electric field corresponds to more alkaline conditions, while a higher magnetic field exhibits more acidic conditions. (b) Rate determination step analysis of HCOOH formation under acidic (dashed line) and alkaline (solid) conditions. The purple line indicates activity restricted by the first electron transfer, while the green line indicates restriction by the second electron transfer. ©Hao Li et al.

The catalysts identified by the model were performed in simulations that monitored activity at different pH levels on a reversible hydrogen electrode (RHE) scale. Researchers investigated jointly₂ Reduction reactions, check how each catalyst was carried out under various conditions. These results provide new insight into the behavior of these catalysts, as calculations from previous literature struggled to accurately explain the effects of pH dependence on electrocatalytic performance.

Furthermore, the simulation results show excellent agreement with actual experimental observations verifying the accuracy of this machine learning technique. This study will help to form a more comprehensive understanding of SN-based catalysts. This will help you to maximize those possibilities. More efficient catalysts bring affordable green fuel production closer to everyday reality.

In the future, the research group plans to optimize the training process of machine learning possibilities to develop a more accurate and universal training framework, thereby better filling the gap between experimental findings and theoretical predictions.

All related experimental and computational data have been uploaded to the Digital Catalyst Platform (DIGCAT), the largest catalyst database and digital platform developed by Hao Li Lab.

The survey results have been published on Advanced functional materials June 26, 2025.

Material characterization and performance testing. (AB) XRD spectrum of SNOs₂ and SNS₂ Before and after co₂RR. (CD) SNO XPS spectra₂ and SNS₂ Before and after the reaction. (EF) SEM images₂: (e) before the reaction and (f) after the reaction. (GH) SEM images for SNS₂: (g) before the reaction and (h) after the reaction. (IJ) SNO HRTEM images₂: Before (i) and (j). (kl) SNS HRTEM images₂: (k) before the reaction and (l) after the reaction. (MN)SNO current density and applicable potential₂ and SNS₂ at different pH levels. (op) Joint Farada efficiency₂SNO's RR₂ and SNS₂ At different potentials of pH = 13. ©Hao Li et al.

Publication details:

Title: Bridging Theory and Experiments: Machine Learning Latently Driven Insights on pH-Dependent CO₂ Reduction in SN-Based Catalysts

Authors: Yuhang Wang, Zelin Wu, Yingfang Jiang, Di Zhang, Qiang Wang, Congwii Wang, Huihui Li, Xue Jia, Jun Fan, and Hao Li

Journal: Advanced Functional Materials

doi:10.1002/adfm.202506314

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