Structural Optimization in Tensor LEED Using a Parameter Tree and $R$-Factor Gradients

TL;DR

This paper introduces a new tensor-LEED surface-structure optimization method using a parameter tree and $R$-factor gradients, significantly reducing computation time and enabling advanced optimization algorithms.

Contribution

It reformulates surface-structure optimization with a tree-based data structure and on-the-fly intensity calculations, improving efficiency and flexibility over previous methods.

Findings

Computing time reduced by over an order of magnitude.

Supports GPU acceleration and gradient-based optimization.

Eliminates limitations of precomputed intensity grids.

Abstract

Quantitative low-energy electron diffraction [LEED $I(V)$] is a powerful method for surface-structure determination, based on a direct comparison of experimentally observed $I(V)$ data with computations for a structure model. As the diffraction intensities $I$ are highly sensitive to subtle structural changes, local structure optimization is essential for assessing the validity of a structure model and finding the best-fit structure. The calculation of diffraction intensities is well established, but the large number of evaluations required for reliable structural optimization renders it computationally demanding. The computational effort is mitigated by the tensor-LEED approximation, which accelerates optimization by applying a perturbative treatment of small deviations from a reference structure. Nevertheless, optimization of complex structures is a tedious process. Here, the problem of surface-structure optimization is reformulated using a tree-based data structure, which helps to avoid redundant function evaluations. In the new tensor-LEED implementation presented in this work, intensities are computed on the fly, eliminating limitations of previous algorithms that are limited to precomputed values at a grid of search parameters. It also enables the use of state-of-the-art optimization algorithms. Implemented in \textsc{Python} with the JAX library, the method provides access to gradients of the $R$ factor and supports execution on graphics processing units (GPUs). Based on these developments, the computing time can be reduced by more than an order of magnitude.