Physics-Informed Symbolic Regression for Predicting the Glass Transition Temperature of Alkali Borate Glasses

[2608.14853] Physics-Informed Symbolic Regression for Predicting the Glass Transition Temperature of Alkali Borate Glasses Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Condensed Matter > Soft Condensed Matter arXiv:2608.14853 (cond-mat) [Submitted on 14 Aug 2026] Title: Physics-Informed Symbolic Regression for Predicting the Glass Transition Temperature of Alkali Borate Glasses Authors: Leonardo dos Santos Vitoria , Marcio Luis Ferreira Nascimento , Susana de Souza Lalic , Daniel Roberto Cassar View a PDF of the paper titled Physics-Informed Symbolic Regression for Predicting the Glass Transition Temperature of Alkali Borate Glasses, by Leonardo dos Santos Vitoria and 3 other authors View PDF HTML (experimental) Abstract: The glass transition temperature ($T_{g}$) of alkali borate glasses is strongly composition-dependent and difficult to predict from first principles due to the structural complexity of the boron network. Here, we apply physics-informed symbolic regression (combining evolutive search with physically meaningful descriptors) to derive an interpretable closed-form expression for $T_{g}$ in the $x\mathrm{M}_2\mathrm{O}\cdot(100-x)\mathrm{B}_2\mathrm{O}_3$ glass family, with M = Li, Na, and K and $x$ expressed in mol%, and subsequently extrapolate it to M = Rb and Cs. The resulting model achieves a root-mean-square error of 14-16 K while maintaining clear physical interpretability, explicitly capturing the interplay among $T_{g}$, structural dissociation energy, and network packing. Critically, models built on the Rigid Unit Packing Fraction (RUPF) yield substantially more realistic $T_{g}$ predictions than those using the conventional Atomic Packing Fraction (APF), as APF overestimates structural rigidity at intermediate compositions. The fitted dissociation energies are further validated against the revised Makishima-Mackenzie model, confirming that the inferred parameters are physically consistent, not merely statistically effective, within the alkali borate family. Finally, Monte Carlo uncertainty quantification reveals that prediction uncertainty is highest in the compositional regions associated with the boron anomaly, directly linking model limitations to a known structural transition in these glasses. This result highlights the potential of physics-informed symbolic regression as a transparent and interpretable alternative to black-box models for property prediction in glass systems. Comments: 21 pages, 3 figures Subjects: Soft Condensed Matter (cond-mat.soft) ; Materials Science (cond-mat.mtrl-sci) Cite as: arXiv:2608.14853 [cond-mat.soft] (or arXiv:2608.14853v1 [cond-mat.soft] for this version) https://doi.org/10.48550/arXiv.2608.14853 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Daniel Cassar [ view email ] [v1] Fri, 14 Aug 2026 19:49:36 UTC (154 KB) Full-text links: Access Paper: View a PDF of the paper titled Physics-Informed Symbolic Regression for Predicting the Glass Transition Temperature of Alkali Borate Glasses, by Leonardo dos Santos Vitoria and 3 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cond-mat.soft < prev | next > new | recent | 2026-08 Change to browse by: cond-mat cond-mat.mtrl-sci References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... 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