Some of the most encouraging results for reaction-enhancing catalysts come from one material in particular: tin (Sn). While Sn's overall utility as a catalyst is well-known, its underlying structure-performance relationship is poorly understood, which limits our ability to maximize its potential. To address this knowledge gap, researchers at Tohoku University's Advanced Institute for Materials Research (WPI-AIMR) used machine learning to characterize Sn catalyst activity. The highly accurate simulations could be a game-changer that helps researchers swiftly and simply design high-performance complex catalysts.
"The reason these catalysts are so important is that they can convert harmful carbon dioxide - CO2 - into carbon-based fuels using renewable electricity, offering a sustainable solution to energy shortages and climate change," explains Hao Li of WPI-AIMR. "The aim of this research is to guide our society towards carbon neutrality."
To closely examine Sn catalysts, they employed machine learning potential to perform large-scale molecular dynamics simulations, successfully capturing the reconstructed configurations of SnO2/SnS2. The approach used data from over 1,000 experimental literature sources to identify various Sn-based catalysts.
"Instead of spending days, months, or even years doing all of these experiments in the lab, we can run these sophisticated, data-driven simulations that can helpfully inform which lab-based experiments to focus our attention on," says Li.
The catalysts identified by the model were run in simulations that monitored their activity at different pH levels at the reversible hydrogen electrode (RHE) scale. The researchers examined the CO2 reduction reaction, to see how each catalyst performed under different conditions. Calculations from previous literature struggled to accurately account for the impact of pH-dependence on electrocatalytic performance, therefore these results provide novel insights into the behaviour of these catalysts.
Furthermore, the simulation results show excellent agreement with actual experimental observations, which validates the accuracy of this machine learning technique. This study helps us form a more comprehensive understanding of Sn-based catalysts, so that their full potential can be brought out. More efficient catalysts bring affordable green fuel production closer to being an everyday reality.
In the future, the research group plans to optimize the training process of the machine learning potential to develop a more accurate and universal training framework, thereby better bridging the gap between experimental findings and theoretical predictions.
All relevant experimental and computational data have been uploaded to the Digital Catalysis Platform (DigCat), the largest catalysis database and digital platform developed by the Hao Li lab.
The findings were published in Advanced Functional Materials on June 26, 2025.