Statistical generic design of glass and optimization: Selective review on oxide glasses

Abstract Designing a single glass composition for a multidimensional property space is challenging, and the difficulty increases with the number of design criteria. Traditionally, the task is accomplished using multiple statistical models that describe the relationships between composition (C) and property (P) values, i.e., C‐P models. Recently, the structure (S)‐property (P) statistical modeling has emerged as a complementary approach. The S‐P modeling approach has also been shown to be a preferred method for modeling glass properties, particularly when a small data set is available, such as in single‐component studies, or when strong nonlinearities exist between composition and properties. The combined model package, C‐S‐P, implements the concept of generic glass design, i.e., designing glass for performance by first selecting a specific or optimized set of glass network structural groups using S‐P models and then transferring the designed structures (genes) to a particular composition using C‐S models. This article reviews a set of supporting cases from the previous C‐S‐P modeling studies of phosphate, silicate, and borosilicate glasses, which are relevant for many critical commercial applications. The methodology for developing the statistical C‐S‐P database is presented, enabling the application of P→S→C to achieve a generic glass design and optimization, targeting multiple design criteria for both performance and processing properties simultaneously.