Abstract
With abundant composition-dependent glass properties data of good quality, machine learning-based models can enable the development of glass compositions with desired properties such as liquidus temperature, viscosity, and Young's modulus using much fewer experiments than would otherwise be needed in a purely experimental exploratory research. In particular, research companies with long track records of exploratory research are in the unique position to capitalize on data-driven models by compiling their earlier internal experiments for research and product development. In this chapter, we demonstrate how Corning has used this unique advantage to develop models based on neural networks and genetic algorithms to predict compositions that will yield a desired liquidus temperature as well as viscosity, Young's modulus, compressive stress, and depth of layer.
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Acknowledgements
Section 33.4.4 was reprinted (adapted) with permission from J.C. Mauro, A. Tandia, K.D. Vargheese, Y.Z. Mauro, and M.M. Smedskjaer: Accelerating the design of functional glasses through modeling, Chemistry of Materials 28, 4267–4277 (2016). Copyright (2016) The American Chemical Society. Adama Tandia would like to thank Russell Magaziner for valuable discussions regarding the content and flow of the document, Deenamma Varghese and Venkatesh Botu for many discussions about applications of machine learning to glass properties predictions, colleagues at Corning, too many to list, for valuables suggestions and feedback during many years of ML tools development and validation.
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Tandia, A., Onbasli, M.C., Mauro, J.C. (2019). Machine Learning for Glass Modeling. In: Musgraves, J.D., Hu, J., Calvez, L. (eds) Springer Handbook of Glass. Springer Handbooks. Springer, Cham. https://doi.org/10.1007/978-3-319-93728-1_33
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