Prediction of the elastic properties and electrical resistance of halide glass based on interpretable machine learning
Source abstract
Abstract
This paper employs regression models based on machine learning to predict the elastic properties and electrical resistance of halide glass, unveiling the composition–property map and enabling swift optimization and rapid iteration.
Only a short abstract or summary was supplied by the source.
Source: crossref · Quality: short
Research context
OpenAlex topics: Glass properties and applications · Metallic Glasses and Amorphous Alloys · Structural Analysis of Composite Materials
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
College of Electronic and Optical Engineering & College of Flexible Electronics (Future Technology), Nanjing University of Posts and Telecommunications, Nanjing 210023, China
The Key Laboratory of Radio and Micro-Nano Electronics of Jiangsu Province, Nanjing 210023, China
Funding
National Natural Science Foundation of China
References (94)
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