Understanding the compositional control on electrical, mechanical, optical, and physical properties of inorganic glasses with interpretable machine learning-Reference-Cited by-同舟云学术

Understanding the compositional control on electrical, mechanical, optical, and physical properties of inorganic glasses with interpretable machine learning

Published:2023-01 Issue: Volume:242 Page:118439
ISSN:1359-6454
Container-title:Acta Materialia
language:en
Short-container-title:Acta Materialia

Author:

Bhattoo Ravinder,Bishnoi Suresh,Zaki Mohd^ORCID,Krishnan N. M. Anoop

Publisher

Elsevier BV

Subject

Metals and Alloys,Polymers and Plastics,Ceramics and Composites,Electronic, Optical and Magnetic Materials

Reference58 articles.

1. Grand challenges in glass science;Mauro;Glass Sci.,2014

2. Two centuries of glass research: historical trends, current status, and grand challenges for the future;Mauro;Int. J. Appl. Glass Sci.,2014

3. Fundamentals of Inorganic Glasses;Varshneya,2019

4. The atomic arrangement in glass;Zachariasen;J. Am. Chem. Soc.,1932

5. Bioactive glasses—structure and properties;Brauer;Angew. Chem. Int. Ed.,2015

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