Unknown

Dataset Information

0

Accurate space-group prediction from composition.


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.

SUBMITTER: Venkatraman V 

PROVIDER: S-EPMC11299606 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature

altmetric image

Publications

Accurate space-group prediction from composition.

Venkatraman Vishwesh V   Carvalho Patricia Almeida PA  

Journal of applied crystallography 20240618 Pt 4


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 classi  ...[more]

Similar Datasets

| S-EPMC7110051 | biostudies-literature
| S-EPMC8805161 | biostudies-literature
| S-EPMC5994942 | biostudies-literature
2020-10-14 | GSE128827 | GEO
| S-EPMC9516681 | biostudies-literature
| S-EPMC9618264 | biostudies-literature
| S-EPMC10834310 | biostudies-literature
| S-EPMC7188683 | biostudies-literature
2015-07-06 | E-GEOD-59515 | biostudies-arrayexpress
| S-EPMC3614465 | biostudies-other