Physics-informed automated surface reconstructing via low-energy electron diffraction based on Bayesian optimization

Xiankang Tang; Ruiwen Xie; Jan P. Hofmann; Hongbin Zhang

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arXiv · 2604.04578

Physics-informed automated surface reconstructing via low-energy electron diffraction based on Bayesian optimization

Xiankang Tang·Ruiwen Xie·Jan P. Hofmann·Hongbin Zhang

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Abstract

Low-energy electron diffraction (LEED) is a cornerstone technique for determining surface atomic structures[heldStructureDeterminationLowenergy2025], yet the quantitative analysis of electron diffraction intensity as a function of incident electron energy -- that is, LEED-\textit{I(V)} analysis -- remains a complex inverse problem. In this work, we tackle quantitative LEED-\textit{I(V)} analysis based on physics-informed Bayesian optimization (BO). By embedding multiple scattering LEED forward models directly into a trust-region BO loop, our approach simultaneously optimizes both structural and experimental parameters, adaptively adjusting trust regions for efficient exploration of complex non-convex parameter spaces without manual intervention. The robustness and scalability of the approach are demonstrated using the Ag(100)-(1$\time$1) and Fe\textsubscript{2}O\textsubscript{3}(1$\overline{1}$02)-(1$\time$1) surfaces as examples. Our work establishes a general framework for solving inverse problems in various characterization techniques, unlocking a physics-informed efficient, reproducible, and autonomous paradigm.

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Xiankang Tang, Ruiwen Xie, Jan P. Hofmann, Hongbin Zhang. 2026-04-06. Physics-informed automated surface reconstructing via low-energy electron diffraction based on Bayesian optimization. https://arxiv.org/abs/2604.04578

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