Learning Self-Supervised Representations of Powder-Diffraction Patterns

Learning Self-Supervised Representations of Powder-Diffraction Patterns

The potential of machine learning (ML) models for predicting crystallographic symmetry information from single-phase powder X-ray diffraction (XRD) patterns is investigated. Given the scarcity of large, labeled experimental datasets, we train our models using simulated XRD patterns generated from cr...

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Bibliographic Details
Main Authors: Shubhayu Das, Markus Vorholt, Andreas Houben, Richard Dronskowski
Format: Article
Language:English
Published: MDPI AG 2025-04-01
Series:Crystals
Subjects:
diffraction
crystallography
machine learning
self-supervised learning
representation learning
Online Access:https://www.mdpi.com/2073-4352/15/5/393
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