ArticleJournal of applied crystallography2024
Accurate space-group prediction from composition.
Article in Journal of applied crystallography, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed.
- Can Machine Learning Predict the Space Group Preference of Organic Molecules?Crystal growth & design · 2026Article
- Machine learning approaches for crystallographic classification from synthetic 2D X-ray diffraction data.Journal of applied crystallography · 2026Article
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Authors and funding
2 authors.
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Abstract
Predicting crystal symmetry simply from chemical composition has remained challenging. Several machine-learning approaches can be employed, but the predictive value of popular crystallographic databases is relatively modest due to the paucity of data and uneven distribution across the 230 space groups. In this work, virtually all crystallographic information available to science has been compiled and used to train and test multiple machine-learning models. Composition-driven random-forest classification relying on a large set of descriptors showed the best performance. The predictive models for crystal system, Bravais lattice, point group and space group of inorganic compounds are made publicly available as easy-to-use software downloadable from https://gitlab.com/vishsoft/cosy.
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