Predicting Activation Energy of Hydrocarbon Dehydrogenation on Au(111) via Machine Learning.

Journal: The journal of physical chemistry letters
Published Date: Jan 15, 2026

Abstract

Activation of C-H bonds in hydrocarbons is fundamental for synthesizing organic functional materials, yet traditional density functional theory (DFT) methods for determining reaction energy barriers are computationally intensive and often suffer from convergence challenges. We report here the construction of a comprehensive DFT-based data set of hydrocarbon dehydrogenation reactions on the Au(111) surface and propose a feature-enhanced graph neural network (F-GNN) that integrates eight chemically informed descriptors with molecular graph representations. This F-GNN model accurately predicts reaction activation energies, outperforming conventional approaches such as the Brønsted-Evans-Polanyi relationship and standalone machine learning models. Our findings demonstrate that combining chemical prior knowledge with data-driven features enables efficient and precise energy barrier prediction, offering a promising strategy to accelerate reaction path screening and mechanistic understanding in surface-catalyzed hydrocarbon transformations.

Authors

  • Tianyu Gao
    State Key Laboratory of Bioinspired Interfacial Materials Science, Institute of Functional Nano & Soft Materials (FUNSOM), Soochow University, Suzhou 215123, China.
  • Yuying Wang
    Data Mining Research Center, Xiamen University, Xiamen, 361005, Fujian, China.
  • Ran Jia
    State Key Laboratory of Inorganic Synthesis and Preparative Chemistry, College of Chemistry, Jilin University, 130023 Changchun, P.R. China.
  • Haiming Zhang
    Liver Transplantation Center, Clinical Research Center for Pediatric Liver Transplantation, State Key Lab of Digestive Health, National Clinical Research Center for Digestive Diseases, Beijing Friendship Hospital, Capital Medical University, 95 Yong'an Road, Xicheng District, Beijing, 100050, China.
  • Miao Xie
    School of Computer Science and Engineering, Yulin Normal University, Yulin, 537000, China. [email protected].
  • William A Goddard Iii
    Materials and Process Simulation Center, California Institute of Technology, Pasadena, California 91125, United States.
  • Lifeng Chi
    State Key Laboratory of Bioinspired Interfacial Materials Science, Institute of Functional Nano and Soft Materials (FUNSOM), Soochow University, Suzhou 215123, China.

Keywords

No keywords available for this article.

External Resources

Popular Topics
Recent Journals