The Kitchin Research Group

The paper “Unifying Theory of Electronic Descriptors of Metal Surfaces upon Perturbation” by Huang et al. presents a novel framework for predicting electronic properties of metal surfaces using interpretable deep learning. Unlike traditional black-box machine learning models, this approach integrates physical insights to enhance interpretability without sacrificing accuracy. The study identifies a previously overlooked factor—orbital resonance in d-electron hopping—which influences the electronic structure of transition and noble metal surfaces alongside known effects like charge transfer, strain, and ligand interactions. By leveraging a physics-infused deep learning model, the authors provide a unified method for predicting electronic descriptors, offering a powerful tool for the rational design of catalytic materials and other functional surfaces in materials science. This work bridges the gap between theory and data-driven approaches, paving the way for more efficient and interpretable materials discovery.

Yang Huang, Shih-Han Wang, Mohith Kamanuru, Luke E. K. Achenie, John R. Kitchin, and Hongliang Xin, Unifying theory of electronic descriptors of metal surfaces upon perturbation, Phys. Rev. B 110, L121404 (2024). https://doi.org/10.1103/PhysRevB.110.L121404.

@article{huang-2024-unify-theor,
  author =       {Yang Huang and Shih-Han Wang and Mohith Kamanuru and Luke E.
                  K. Achenie and John R. Kitchin and Hongliang Xin},
  title =        {Unifying Theory of Electronic Descriptors of Metal Surfaces
                  Upon Perturbation},
  journal =      {Physical Review B},
  volume =       110,
  number =       12,
  pages =        {L121404},
  year =         2024,
  doi =          {10.1103/physrevb.110.l121404},
  url =          {http://dx.doi.org/10.1103/PhysRevB.110.L121404},
  DATE_ADDED =   {Wed Jan 29 19:39:29 2025},
}

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