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Hybrid physics-machine learning models for quantitative electron diffraction refinements.

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Bibliographic Details
Title: Hybrid physics-machine learning models for quantitative electron diffraction refinements.
Authors: Malik SA; OATML, Department of Computer Science, University of Oxford, Oxford, UK., Doherty TAS; OATML, Department of Computer Science, University of Oxford, Oxford, UK. td404@cam.ac.uk.; Department of Materials Science and Metallurgy, University of Cambridge, Cambridge, UK. td404@cam.ac.uk., Colmey B; Department of Materials Science and Metallurgy, University of Cambridge, Cambridge, UK., Roberts SJ; Machine Learning Research Group, Department of Engineering Science, University of Oxford, Oxford, UK., Gal Y; OATML, Department of Computer Science, University of Oxford, Oxford, UK. yarin.gal@cs.ox.ac.uk., Midgley PA; Department of Materials Science and Metallurgy, University of Cambridge, Cambridge, UK. pam33@cam.ac.uk.
Source: Nature communications [Nat Commun] 2026 Apr 11; Vol. 17 (1). Date of Electronic Publication: 2026 Apr 11.
Publication Type: Journal Article
Journal Info: Publisher: Nature Pub. Group Country of Publication: England NLM ID: 101528555 Publication Model: Electronic Cited Medium: Internet ISSN: 2041-1723 (Electronic) Linking ISSN: 20411723 NLM ISO Abbreviation: Nat Commun Subsets: MEDLINE; PubMed not MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:2041-1723
DOI:10.1038/s41467-026-71673-9