Glass formulation and composition optimization with property models: A review

Lu, Xiaonan; Vienna, John D.; Du, Jincheng

doi:10.1111/jace.19333

Title: Glass formulation and composition optimization with property models: A review

Journal Article · 30 July 2023 · Journal of the American Ceramic Society

DOI: https://doi.org/10.1111/jace.19333 · OSTI ID:1993108

^[1];

^[2]

Energy and Environment Directorate Pacific Northwest National Laboratory Richland Washington USA
Department of Materials Science and Engineering University of North Texas Denton Texas USA

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.

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Sponsoring Organization:: USDOE

Grant/Contract Number:: AR0001613

OSTI ID:: 1993108

Journal Information:: Journal of the American Ceramic Society, Journal Name: Journal of the American Ceramic Society Journal Issue: 3 Vol. 107; ISSN 0002-7820

Publisher:: Wiley-BlackwellCopyright Statement

Country of Publication:: United States

Language:: English

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Glass Property Models and Constraints for Estimating the Glass to be Produced at Hanford by Implementing Current Advanced Glass Formulation Efforts Vienna, John D.; Kim, Dong-Sang; Skorski, Daniel C. https://doi.org/10.2172/1170502	report	July 2013
Database and Interim Glass Property Models for Hanford HLW and LAW Glasses Vienna, John D.; Kim, Dong-Sang; Hrma, Pavel R. https://doi.org/10.2172/15003540	report	September 2002
2016 Update of Hanford Glass Property Models and Constraints for Use in Estimating the Glass Mass to be Produced at Hanford by Implementing Current Enhanced Glass Formulation Efforts Vienna, John; Piepel, Gregory; Kim, Dong-Sang https://doi.org/10.2172/1772236	report	September 2016
Glass Property-Composition Models for Support of Hanford WTP LAW Facility Operation Vienna, John; Heredia-Langner, Alejandro; Cooley, Scott https://doi.org/10.2172/1862823	report	March 2022
Preliminary Enhanced LAW Glass Formulation Algorithm Lumetta, Nicholas A.; Kim, Dong Sang; Vienna, John D. https://doi.org/10.2172/1862824	report	February 2022
Preliminary Enhanced LAW Glass Formulation Algorithm Lumetta, Nicholas A.; Kim, Dong Sang; Vienna, John D. https://doi.org/10.2172/2203566	report	January 2023
NMR Spectroscopy in Glass Science: A Review of the Elements Youngman, Randall Materials, Vol. 11, Issue 4 https://doi.org/10.3390/ma11040476	journal	March 2018
Machine Learning with Gradient-Based Optimization of Nuclear Waste Vitrification with Uncertainties and Constraints Gunnell, LaGrande Lowell; Manwaring, Kyle; Lu, Xiaonan Processes, Vol. 10, Issue 11 https://doi.org/10.3390/pr10112365	journal	November 2022
GEKKO Optimization Suite Beal, Logan; Hill, Daniel; Martin, R. Processes, Vol. 6, Issue 8 https://doi.org/10.3390/pr6080106	journal	July 2018
Calibration of Machine Learning Models Bella, Antonio; Ferri, Cèsar; Hernández-Orallo, José Handbook of Research on Machine Learning Applications and Trends https://doi.org/10.4018/978-1-60566-766-9.ch006	book	January 2010

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