From Noisy STEM to Crystal Structure: Evidence-Structure CoDiffusion under Composition Constraints

Abstract

By directly resolving lattice periodicity and atomic columns, scanning transmission electron microscopy (STEM) offers rich structural cues, yet recovering a simulation-ready crystal structure from a single noisy STEM image remains an ill-posed inverse problem. In realistic acquisitions, corruption can obscure geometry-aligned cues, and composition-only structure generation is highly multimodal, yielding many plausible 2D slab candidates. We cast this task as a coupled inference problem that separates evidence recovery from structure inference under a minimal forward model. We introduce STEM2Crystal CoDiffusion (SCCD), a dual-diffusion framework that explicitly separates evidence recovery from structure inference and couples them via bidirectional feature exchange. SCCD comprises (i) an evidence diffusion branch that denoises a structure-aligned, mask-like evidence map conditioned on the noisy STEM image, and (ii) a crystal diffusion branch that jointly denoises the lattice and atomic coordinates under the given composition constraint. A bidirectional co-diffusion update enables iterative refinement: crystallographic context regularizes evidence denoising, while denoised image evidence provides noise-robust geometric cues that sharpen structure inference. For controlled evaluation, we release a large-scale synthetic benchmark with explicit composition constraints, controlled noise regimes, and projection-derived supervision signals. Across multiple complementary metrics and all noise levels, SCCD consistently outperforms strong baselines.