An interpretable stacking ensemble learning framework guides the design of efficient and sustainable CO2-to-methanol catalysts

Key Points

• The framework improves the design of CO2-to-methanol catalysts, leading to more efficient processes for carbon capture and utilization.
• Machine learning techniques were employed to analyze various parameters influencing catalyst efficiency and sustainability.
• The stacking ensemble method enhances model interpretability, providing insights into the key factors affecting catalyst performance.
• This approach may enable more sustainable chemical processes, potentially reducing carbon emissions significantly.