Glass formulation and composition optimization with property models: A review

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.

Automated summary

Glass is a versatile material with various applications. This paper focuses on the development of precise property models for glass compositions using large databases and efficient formulation approaches. It reviews analytical and numerical models based on composition-structure-property relations and discusses aspects such as data collection, model fitting, feature extraction, model evaluation, and uncertainty quantification. The paper also summarizes advances in the glass optimization framework and available tools and provides an outlook for further development in glass property models and formulation approaches.