High Performance Catalyst Design by Artificial Intelligence for Acetylene Hydrochlorination

ABSTRACT The rapid development of artificial intelligence (AI) has brought transformative contributions to society, revolutionizing various fields through its advanced computational capabilities and data‐driven approaches. In the field of materials science, AI has become a powerful tool that significantly accelerates the discovery of new materials by providing predictive insights, optimizing workflows, and guiding experimental design. These advancements have paved the way for more efficient and sustainable innovation processes. Herein, we developed a model called AI‐driven ligand screening model based on a deep neural network to guide the catalyst design. With an accuracy of 81% on the validation dataset, the model screened 8033 ligands and identified 31 promising candidates for catalyst modification. By utilizing this AI‐driven approach, we successfully developed two mercury‐free acetylene hydrochlorination catalysts with excellent catalytic performance (95% vinyl chloride yield). This work highlights the potential of AI in simplifying catalyst development and enhancing material performance. The AI‐driven ligand screening model demonstrates its capability to accelerate the discovery process, offering a high‐efficiency framework for optimizing catalysts and advancing sustainable materials research.