2025 AIChE Annual Meeting

(705d) Advancing Catalysis Theory with Theory-Infused Deep Learning

Authors

Yang Huang, Auburn University
Siwen Wang, Virginia Polytechnic Institute and State University
Luke Achenie, National Science Foundation
Hongliang Xin, Virginia Tech
Despite recent advances of data acquisition and algorithms development, machine learning (ML) faces tremendous challenges to being adopted in practical catalyst design, largely due to its limited generalizability and poor explainability. We developed a theory-infused neural network (TinNet) approach that integrates deep learning algorithms with catalysis theory. Incorporation of scientific knowledge of physical interactions into learning from data opens up new avenues for interpretable discovery of novel motifs with desired catalytic properties. The TinNet framework offers a robust platform for transforming ab initio data into physicochemical insights, enabling the design of novel catalytic materials. Its architecture, deeply rooted in the physics of electronic interactions, transcends algorithmic boundaries. TinNet not only sheds light on the fundamental characteristics of active sites but also enhances prediction accuracy in harmony with the physical principles governing catalytic surfaces.