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To mitigate this limitation, the dataset was augmented to 1336 samples using the synthetic minority oversampling technique, enabling improved learning of underrepresented dimensionality classes while preserving chemically meaningful feature relationships. We developed interaction-based descriptors designed to capture coupled steric and polarity effects relevant to dimensionality prediction, which are not readily captured by standard single-parameter or composition-only descriptors. These descriptors are integrated into a multi-stage workflow combining feature selection, ensemble stacking, and performance optimization. Our approach significantly improves\n                    <jats:italic>F<\/jats:italic>\n                    1-scores for underrepresented classes, achieving robust cross-validation performance across all dimensionalities. This work demonstrates a generalizable strategy for extracting reliable and interpretable structure\u2013dimensionality relationships from limited experimental data, enabling pre-synthesis screening of organic cations and providing a practical blueprint for small-data ML in hybrid materials systems.\n                  <\/jats:p>","DOI":"10.1088\/2632-2153\/ae62cb","type":"journal-article","created":{"date-parts":[[2026,4,21]],"date-time":"2026-04-21T22:54:42Z","timestamp":1776812082000},"page":"035011","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Enhancing dimensionality prediction in hybrid metal halides via feature engineering and class-imbalance mitigation"],"prefix":"10.1088","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0081-8497","authenticated-orcid":true,"given":"Mariia","family":"Karabin","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Isaac","family":"Armstrong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-9568-9733","authenticated-orcid":true,"given":"Leo","family":"Beck","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-3007-1323","authenticated-orcid":true,"given":"Paulina","family":"Apanel","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8805-8327","authenticated-orcid":true,"given":"Markus","family":"Eisenbach","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"David B","family":"Mitzi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hanna","family":"Terletska","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hendrik","family":"Heinz","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"266","published-online":{"date-parts":[[2026,5,7]]},"reference":[{"key":"mlstae62cbbib1","doi-asserted-by":"publisher","first-page":"90","DOI":"10.1038\/s41524-021-00551-3","type":"journal-article","article-title":"Accelerated design and discovery of perovskites with high conductivity for energy applications through machine learning","volume":"7","author":"Priya","year":"2021","journal-title":"npj Comput. 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