{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/machine-learning-modeling-of-the-atomic","title":"Machine learning modeling of the atomic structure and physical properties of alkali and alkaline-earth aluminosilicate glasses and melts","arxiv_id":"2304.12123","date":"2023-04-24","proceeding":null,"authors":["Charles Le Losq","Barbara Baldoni"],"abstract":"The first version of the machine learning greybox model i-Melt was trained to predict latent and observed properties of K$_2$O-Na$_2$O-Al$_2$O$_3$-SiO$_2$ melts and glasses. Here, we extend the model compositional range, which now allows accurate predictions of properties for glass-forming melts in the CaO-MgO-K$_2$O-Na$_2$O-Al$_2$O$_3$-SiO$_2$ system, including melt viscosity (accuracy equal or better than 0.4 log$_{10}$ Pa$\\cdot$s in the 10$^{-1}$-10$^{15}$ log$_{10}$ Pa$\\cdot$s range), configurational entropy at glass transition ($\\leq$ 1 J mol$^{-1}$ K$^{-1}$), liquidus ($\\leq$ 60 K) and glass transition ($\\leq$ 16 K) temperatures, heat capacity ($\\leq$ 3 \\%) as well as glass density ($\\leq$ 0.02 g cm$^{-3}$), optical refractive index ($\\leq$ 0.006), Abbe number ($\\leq$ 4), elastic modulus ($\\leq$ 6 GPa), coefficient of thermal expansion ($\\leq$ 1.1 10$^{-6}$ K$^{-1}$) and Raman spectra ($\\leq$ 25 \\%). Uncertainties on predictions also are now provided. The model offers new possibilities to explore how melt/glass properties change with composition and atomic structure.","url_abs":"https://arxiv.org/abs/2304.12123v2","url_pdf":"https://arxiv.org/pdf/2304.12123v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"machine-learning-modeling-of-the-atomic","repo_url":"https://github.com/charlesll/i-melt","is_official":0,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}