Prediction of Catalytic Performance of Metal Oxide Catalysts for Alkyne Hydrogenation Reaction Based on Machine Learning

In the field of catalyst design, machine learning is gaining significant attention, especially in situations where data is limited. Facing this challenge, we have developed a neural network model that enhances predictive accuracy through the careful selection of catalyst descriptors and feature engineering, aiming to predict the conversion rate and selectivity of the acetylene semi-hydrogenation reaction. Our model has identified promising metal oxide catalysts, such as CuO, ZnO, and V2O5, for the acetylene semi-hydrogenation reaction, and these predictions have been experimentally validated. Among them, CuO achieved an acetylene conversion of 99.6% and an ethylene selectivity of 90% at 50-100°C, which is unprecedented at the reaction space velocity we tested. This high activity at relatively low temperatures indicates a more promising industrial application. Looking ahead, machine learning will play a pivotal role in catalyst design, accelerating the discovery and industrial application of new materials.