Proposed Bayesian optimization-based framework for automating LEED-based surface reconstruction

How to apply this

One can try to use LEED data to automatically search candidate surface reconstructions with Bayesian optimization.

1. Collect LEED patterns, and prepare a comparable set of candidate surface structures and their corresponding calculated or experimental scores.
2. Define features for the candidate structures and a fitness function, then set up a surrogate model for Bayesian optimization.
3. Train the model with initial samples, and use the acquisition function to sequentially select the next candidate structure to evaluate.
4. Feed newly evaluated results back into the model and iterate, checking converged structure candidates together with uncertainty.

Data needed

Low-energy electron diffraction (LEED) experimental patterns, an initial set of candidate surface structures, and a diffraction fit score or cost-function value for each candidate are needed.

Checking the result

Compare the final structure obtained by automatic search with independent structural refinement or additional LEED simulation, and, if possible, with other surface analysis results to verify reproducibility.

What can go wrong

Because multiple candidates can match LEED patterns equally well, the most common failure is that the surrogate model becomes trapped in a local optimum or overtrusts structures outside the data range.

이 가이드는 원문의 방법을 바탕으로 AI가 제안한 적용 방향입니다. 원문에 적힌 내용이 아니며 같은 결과가 재현된다는 보장도 아닙니다.