Machine Learning on a Synergistic Transition Metal Dual-Atom Surface for Efficient Decomposition of Ammonia.

Hospital-Based Medicine Pediatrics Primary Care State Required CME
Journal: Small methods
Published Date: Jun 12, 2025

Abstract

The technology of H production through NH decomposition is of great importance for finding clean energy alternatives to fossil fuels. Here, a framework for screening dual-atom catalysis by integrating machine learning (ML) with high-throughput (HT) calculations to predict the catalytic performance of dual-atom systems for NH decomposition is designed. First-principles-based HT calculations are conducted on 62 randomly selected systems for intermediate steps. Then, feature engineering is employed to obtain features with high importance and low correlation. The results of the HT calculations are subsequently used as a training set to train the ML model. The well-trained model is subsequently used to predict the catalytic performance of 2187 structures. Several potentially good dual-atom catalysts (RuMo─O─C, ScOs─N─C, and OsV─N─C) for NH decomposition are obtained. Finally, the density of states and differential charge analysis show the presence of the synergistic catalytic process in these dual-atom catalysts.

Authors

  • Gaoxiang He
    National Laboratory of Solid State Microstructures, School of Physics, Nanjing University, 22 Hankou Road, Nanjing, 210093, China.
  • Huihui Yan
    Collaborative Innovation Center of Yangtze River Delta Region Green Pharmaceuticals, Zhejiang University of Technology, Hangzhou, 310014, P. R. China.
  • Rongli Fan
    School of Biological and Chemical Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, China.
  • Mingyue Zhao
    Jiangsu Key Laboratory of Nano Technology, College of Engineering and Applied Sciences, Nanjing University, 22 Hankou Road, Nanjing, 210093, China.
  • Jianming Liu
    Department of Anesthesiology, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai, China. [email protected].
  • Yong Zhou
    National Institutes for Food and Drug Control, Beijing, 100050, China.
  • Zhigang Zou
    National Laboratory of Solid State Microstructures, School of Physics, Nanjing University, 22 Hankou Road, Nanjing, 210093, China.
  • Zhaosheng Li
    Department of Neurology, School of Medicine, The Fourth Affiliated Hospital of Zhejiang University, Yiwu, China.

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