ED-CSP: Crystal Structure Prediction from Electron Diffraction
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. To tra...
| Authors | Germain Poloudenny, Arnaud Demortière, Yaël Frégier |
|---|---|
| Primary category | cs.LG |
| Published | 6 August 2026 |
| Last updated at source | 27 August 2026 |
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