What is it about?
Solving crystal structures from X-ray powder diffraction (XRPD) patterns is challenging for low-crystallinity compounds or nanomaterials. We overcome this limitation by pre-processing XRPD patterns through deep-learning to gain angular resolution.
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Why is it important?
With our approach we enable the ab initio structure determination by XRPD of nanomaterials and low-crystallinity compounds
Perspectives
We envisage that a deep-learning pre-processing of XRPD profiles followed by a data treatment by standard crystallographic methods would push the actual limits of the process of crystal structure solution from X-ray powder data.
PhD Dr. Rocco Caliandro
CNR - Institute of Crystallography
Read the Original
This page is a summary of: New frontiers in the structural analysis of nanomaterials: a deep-learning leap to bridge the gap in crystalline order, IUCrJ, October 2026, International Union of Crystallography,
DOI: 10.1107/s2052252526008444.
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