This project explores the intersection of mathematics, crystallography, and machine learning by developing a method to detect symmetry groups of tilings (crystals). The approach originates from a mathematical technique I introduced in [1], which can be effectively modeled using classical machine learning methods. The work aligns with the principles of explainable AI, as preliminary results suggest the existence of a precise conjecture that should be provable based on data-driven modeling. This poster presents the methodology, key findings, and potential directions for collaboration in bridging theoretical mathematics with computational techniques in crystallography.

[1] Growth functions of periodic space tessellations, with Jakub Malinowski, Zbigniew Dauter and Mariusz Jaskolski, Acta Crystallogr., Sect. A: Found. Adv. (2025), Vol. 81, No. 1, 64-81.