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Meeting Materials Science & Technology 2019
Symposium Data Science for Material Property Interpretation
Presentation Title Python For Glass Genomics (PyGGi): A Machine Learning Package to Predict the Properties of Glasses
Author(s) Hargun Singh, R. Ravinder, Pratik Bhaskar, Hariprasad Kodamana, N. M. Anoop Krishnan
On-Site Speaker (Planned) N. M. Anoop Krishnan
Abstract Scope Glasses are archetypical disordered materials that can be formed by the fast quenching of a liquid. Due to the disordered structure, glasses can accommodate a wide-range of compositions with almost any element in the periodic table. This makes it extremely challenging to predict the mechanical properties of glasses, as they exhibit highly non-linear behavior with respect to the glass composition. Herein, we develop a python-based package, namely Python for Glass Genomics (PyGGi), that can be used to predict the properties for a wide range of oxide glasses. Predictive models are developed using machine learning models such as neural networks and Gaussian process regression. Further, an interface is developed that can easily allow the users to choose glass compositions of interest, predict the properties, plot them, and finally, download them. Development of such packages can be highly useful, both for industry and academia, to develop novel compositions for targeted applications.
Proceedings Inclusion? Definite: At-meeting proceedings

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P2-8: Evaluation for the Quality of Flake Graphite Cast Iron and Spheroidal Graphite Cast Iron by Tapping Test with Using Artificial Intelligence
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