Structure-Informed Design of Float-Compatible Thin Glass Supported by Data-Driven Screening - Gosterislioglu, Yekta Ates; Arslan, Banu; Sokmen, Ilkay; Celikbilek Ersundu, Miray; Ersundu, Ali Ercin (WILEY, 2026)
Motivated by growing demand in electronics, automotive, photovoltaic, and architectural applications, thin-glass products offer substantial potential for material savings and reduced environmental impact, yet their widespread adoption is limited by mechanical instability and reliance on non-float manufacturing routes. In this work, we establish a comprehensive framework for the design of mechanically robust thin-glass compositions compatible with conventional float-glass production. The approach combines (i) structure-informed, composition-structure-property-driven design and (ii) data-driven screening based on artificial intelligence-assisted predictive modeling. While the data-driven approach is used to efficiently explore a broad compositional space and identify candidate glasses with high intrinsic elastic modulus and hardness, the primary optimization of chemical strengthening response and processability is achieved through structure-informed design. Glass compositions within the SiO2-Al2O3-B2O3-RO-R ' 2O system (R = Mg, Ca; R ' = Li, Na, K) were designed to target chemical temperability, mechanical durability, and float-line processability, and were evaluated through thermal, optical, structural, and mechanical analyses. The optimized compositions achieved stress layer depths up to similar to 40 & micro;m after chemical tempering, enabling high mechanical performance at reduced thickness. The best-performing glasses exhibited a similar to 10% increase in fracture load and a similar to 25% increase in surface hardness at a thickness of 0.75 mm. When benchmarked against standard 1.6 mm soda-lime-silica glass, this corresponds to a reduced areal weight of 1.85 kg m-2 (similar to 50% lower mass) and an estimated similar to 60% reduction in process-related CO2 emissions per square meter, based on reduced material usage within the defined boundary. By integrating data-driven screening with structure-informed composition design, this work establishes a scalable pathway for next-generation thin flat glass development, supporting environmentally sustainable and energy-efficient glass manufacturing.
Keywords: artificial intelligence-assisted predictive modeling; chemical strengthening; composition-structure-property relationships; sustainable glass manufacturing; thin flat glass; ALKALI ALUMINOSILICATE GLASSES; PROPERTY RELATIONSHIPS; ELASTIC PROPERTIES; STRENGTH; RESISTANCE; STATE; Materials Science