{"slug": "ed-csp-crystal-structure-prediction-from-electron-diffraction", "title": "ED-CSP: Crystal Structure Prediction from Electron Diffraction", "summary": "Researchers introduced ED-CSP, a machine learning framework that predicts 3D crystal structures from chemical composition, atom count, and electron diffraction spot sets, achieving a 57.49% structural match rate (MR@5) on 2,075 held-out CHILI-100K materials, outperforming PXRDGen's 52.92%. Scaling training data to one million structures raised MR@5 to 66.27%, and on 1,024 compositions absent from the training library the model still achieved 53.52% MR@5, demonstrating generative capability beyond retrieval. The accompanying ED-CS dataset includes 4.85 million simulated multi-view ED crystal structures, establishing a benchmark for generative crystal structure prediction from sparse ED observations.", "body_md": "arXiv:2608.06448v1 Announce Type: new\nAbstract: Recovering a periodic 3D crystal structure from sparse, unindexed electron diffraction (ED) observations is a challenging generative inverse problem. Existing ED-based learning methods mainly predict crystallographic labels, reconstruct structures from indexed reflections, or retrieve candidates from finite structure libraries. Here, we introduce ED-CSP, a machine learning framework that predicts crystal structures from chemical composition, atom count, and multiple detector-plane ED spot sets. ED-CSP combines a relational set encoder, permutation-invariant multi-view aggregation, and a periodic flow generator to jointly predict lattice parameters and fractional atomic coordinates.\nTo train the model, we construct ED-CS, a dataset of 4.85 million simulated multi-view ED crystal structures, deduplicated across seven materials repositories and filtered to exclude CHILI-100K overlaps. On 2,075 held-out CHILI-100K materials, ED-CSP trained only on CHILI achieves a structural match rate of 57.49% MR@5, outperforming PXRDGen (52.92%), a state-of-the-art crystal structure prediction model conditioned on powder X-ray diffraction. Scaling training data further improves performance: initializing from a one-million-structure precursor raises MR@5 to 66.27%. On 1,024 compositions absent from the training retrieval library, the model still achieves 53.52% MR@5, demonstrating true generative capability beyond exact-formula retrieval. Replacing target ED observations with diffraction from non-isomorphic structures of identical composition decreases MR@5 by 22.09 percentage points, confirming that predictions depend on the input diffraction patterns rather than composition alone. ED-CSP and ED-CS establish a benchmark for generative crystal structure prediction from sparse ED observations and provide a foundation for future transfer to experimental data.", "url": "https://wpnews.pro/news/ed-csp-crystal-structure-prediction-from-electron-diffraction", "canonical_source": "https://arxiv.org/abs/2608.06448", "published_at": "2026-08-10 04:00:00+00:00", "updated_at": "2026-08-10 04:12:48.620913+00:00", "lang": "en", "topics": ["machine-learning", "generative-ai"], "entities": ["ED-CSP", "ED-CS", "CHILI-100K", "PXRDGen"], "alternates": {"html": "https://wpnews.pro/news/ed-csp-crystal-structure-prediction-from-electron-diffraction", "markdown": "https://wpnews.pro/news/ed-csp-crystal-structure-prediction-from-electron-diffraction.md", "text": "https://wpnews.pro/news/ed-csp-crystal-structure-prediction-from-electron-diffraction.txt", "jsonld": "https://wpnews.pro/news/ed-csp-crystal-structure-prediction-from-electron-diffraction.jsonld"}}