The DOI matches its Crossref record; 19 of the 19 references it cites were found in a registry or an index.
Accelerating the Design of Functional Glasses through Modeling
Abstract
Functional glasses play a critical role in current and developing technologies. These materials have traditionally been designed empirically through trial-and-error experimentation. However, here we report recent advancements in the design of new glass compositions starting at the atomic level, which have become possible through an unprecedented level of understanding of glass physics and chemistry. For example, new damage-resistant glasses have been developed using models that predict both manufacturing-related attributes (e.g., viscosity, liquidus temperature, and refractory compatibility), as well as the relevant end-use properties of the glass (e.g., elastic moduli, compressive stress, and damage resistance). We demonstrate how this approach can be used to accelerate the design of new industrial glasses for use in various applications. Through a combination of models at different scales, from atomistic through empirical modeling, it is now possible to decode the “glassy genome” and efficiently design optimized glass compositions for production at an industrial scale.
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Explainable machine learning for scientific insights and discoveries
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Metadata
- Authors
- Mauro, J. C., Tandia, A., Vargheese, K. D., Mauro, Y. Z., & Smedskjaer, M. M.
- Venue
- Chemistry of Materials
- Publisher
- American Chemical Society (ACS)
- Published
- 2016-06-14
- Volume
- 28
- Issue
- 12
- Pages
- 4267-4277
- ISSN
- 0897-4756 (ISSN-L)
- 1520-5002
- OpenAlex
- W2411329972
- Cited by
- 256
- References
- 19
- FWCI
- 22.89
- Type
- journal article
- Language
- en
- Open access
- closed
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