EXTENDED ABSTRACT: The demand for high-performance displays is driving the development of alkali-free aluminosilicate glass substrates with extremely low strain variation during manufacturing. To achieve the necessary balance of physical properties to resist strain, the traditional compositional design methods for glass substrates like trial-and-error and classical computational techniques should be optimized. As an alternative approach, machine learning (ML) algorithms have emerged for designing new glass compositions. In this work, we conduct ML research focused on the compositional design for high-performance alkali-free aluminosilicate glass. We employ ML algorithms to predict five physical properties that are key to the performance of alkalinefree aluminosilicate glass. By using only small-tomedium dataset sizes, our model reaches a high coefficient of determination of 0.9879. We further explored the relationship between the composition, structure, and properties of alkali-free aluminosilicate glass through experiments and molecular dynamics simulations.
Keywords：Alkali-free aluminosilicate glass; Machine learning; Structure; Mechanical properties
REFERENCES:
[1] J. Zhu, L. Ding*, G. Sun*, L. Wang. Accelerating design of glass substrates by machine learning using small-to-medium datasets, Ceramics International, 2024;50:3018-3025