Glass formulation and composition optimization with property models: A review

Abstract

Glass is a versatile material with a remarkable history and many practical applications. It plays a critical role in our everyday lives, the advancement of science, and the development of many technologies. The Edisonian type trial‐and‐error method was commonly used for conventional design of glass compositions, which was time‐consuming and costly. With the urgent need to develop new glass compositions for technology applications rapidly, it has become necessary to develop precise property models with predictive powers using large databases and efficient formulation approaches. This paper reviews the design of glass compositions using these analytical and numerical models of composition–structure–property relations of glasses, some based on large databases and machine learning approaches. Aspects of data collection, model fitting, feature extraction, model evaluation, and uncertainty quantification will be covered. Furthermore, advances in the glass optimization framework and available tools are summarized with examples. The outlook and perspective for further glass property model development and formulation approaches are discussed.

Predicting spectroscopic properties of quaternary phosphate laser glasses

“…Only peaks belonging to the Q 2 group are observed (Figure 2B), further confirming our previous findings. Two subpeaks at −25 and −38 ppm obtained by the peak deconvolution for sample 8 correspond to the Q 2 group in C and E, marked as Q 2 8-C and Q 2 8-E , respectively. 21,22 For the relatively integrated intensity of Q 2 8-C , the experimental value (36.7%) agrees well with the value (38.2%) calculated by the phase diagram approach with an error of −3.93%.…”

“…Two subpeaks at −25 and −38 ppm obtained by the peak deconvolution for sample 8 correspond to the Q 2 group in C and E, marked as Q 2 8-C and Q 2 8-E , respectively. 21,22 For the relatively integrated intensity of Q 2 8-C , the experimental value (36.7%) agrees well with the value (38.2%) calculated by the phase diagram approach with an error of −3.93%. The full width at half maximum of the Q 2 group peak for E is larger than that for C, and the chemical shift is also different.…”

Towards informatics-driven design of nuclear waste forms

“…55 While signicant advances have been made in applying AI/ ML methods toward modeling nuclear waste forms, approaches that utilize such methods for the design of entirely novel waste forms are not well established. Some recent works have explored the use of AI/ML to design optimal waste glass compositions, 56,57 but a unied framework for an informatics-driven design, especially in previously-unexplored chemistries encompassing ceramics, glasses, and other types of waste forms is lacking.…”