Abstract
Glass modeling includes unique advantages and challenges with respect to other fields of materials modeling owing to lack of long-range order, strong dependence on temperature and pressure history, statistical nature of glass-forming liquid, and the availability of almost the entire periodic table for constituents in glass. In this chapter, we introduce a range of methods used for glass modeling and overcoming these challenges. We first briefly compare how glass modeling is different from crystalline materials. Next, we briefly outline some of the techniques used for modeling glass and finally present the outstanding challenges in glass modeling and design. As glass modeling merges empirical techniques (i.e., data-driven machine learning, finite element models for mechanical and acoustic properties, composition/property/processing relationships) with fundamental physical methods (i.e., statistical physics, diffusion, first principles quantum mechanical theories, energy landscapes), many orders of magnitude in time- and length scales may be simultaneously modeled across vast composition spaces whose experimental exploration would be prohibitively expensive.
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Onbaşlı, M.C., Mauro, J.C. (2019). Modeling the Relaxation Behavior of Glasses for Display Applications. In: Andreoni, W., Yip, S. (eds) Handbook of Materials Modeling. Springer, Cham. https://doi.org/10.1007/978-3-319-50257-1_99-2
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DOI: https://doi.org/10.1007/978-3-319-50257-1_99-2
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Publisher Name: Springer, Cham
Print ISBN: 978-3-319-50257-1
Online ISBN: 978-3-319-50257-1
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Latest
Modeling of Glasses: an Overview- Published:
- 05 October 2019
DOI: https://doi.org/10.1007/978-3-319-50257-1_99-3
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Modeling the Relaxation Behavior of Glasses for Display Applications
- Published:
- 18 February 2019
DOI: https://doi.org/10.1007/978-3-319-50257-1_99-2
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Original
Modeling the Relaxation Behavior of Glasses for Display Applications- Published:
- 25 January 2019
DOI: https://doi.org/10.1007/978-3-319-50257-1_99-1