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The most innovative software section is the Developer\u2019s Toolkit (DT), which is useful for building up ML models in a user-friendly, no-code environment. In this first version of the software, the DT has been equipped with a fully trainable SoftMax Regressor algorithm, paired with a Stochastic Gradient Descent optimizer. Two additional operational sections are included: 1) Mineral Classifier tool, for achieving supervised or unsupervised mineral classifications based on three classifier types: (a) pre-trained, for user-developed custom models; (b) ROI-based, for supervised algorithms that rely on user-drawn regions of interest (ROIs); and (c) unsupervised, for clustering algorithms; 2) Phase Refiner tool, for removing noisy pixels from classification results through (a) a basic mode, that applies a maximum frequency filter to smooth the entire image; and (b) an advanced mode, which allows class-by-class refinements using morphological image processing. Classified mineral maps, if validated, can be employed in the DT for updating existent ML models or developing new ones tailored for the characterization of samples acquired with specific instrument (e.g., SEM, EPMA) at precise operational conditions (e.g., current, dwell time, pixel size). X-Min Learn\u2019s interactive widgets also allow processing standard image files to populate pixel-wise mineral ground truth datasets. This makes it a versatile and autonomous tool for developing custom ML models from mineral X-ray maps data.<\/jats:p>","DOI":"10.1007\/s12145-026-02149-z","type":"journal-article","created":{"date-parts":[[2026,5,22]],"date-time":"2026-05-22T05:26:17Z","timestamp":1779427577000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["X-Min Learn: machine learning-powered mineral identification from X-ray maps data"],"prefix":"10.1007","volume":"19","author":[{"given":"Alberto","family":"D\u2019Agostino","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5802-6922","authenticated-orcid":false,"given":"Gaetano","family":"Ortolano","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Roberto","family":"Visalli","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michele","family":"Zucali","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rosolino","family":"Cirrincione","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,5,22]]},"reference":[{"issue":"8","key":"2149_CR1","doi-asserted-by":"publisher","first-page":"1295","DOI":"10.3390\/electronics9081295","volume":"9","author":"M Ahmed","year":"2020","unstructured":"Ahmed M, Seraj R, Islam SMS (2020) The k-means algorithm: a comprehensive survey and performance evaluation. 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