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Predicting shear modulus property using materials informatics

Publication Type : Conference Proceedings

Publisher : AIP Publishing

Source : AIP Conference Proceedings

Url : https://doi.org/10.1063/5.0228632

Campus : Bengaluru

School : School of Engineering

Year : 2024

Abstract : In this study, a dataset comprising 2574 compositions was extracted from a Materials database. After cleansing the data, the focus was on predicting the relationship between composition and the shear modulus property. This was accomplished by employing the Composition Based Feature Vector (CBFV) technique, using appropriate Classical Machine Learning Algorithms. Additionally, a Deep Neural Network was also employed for further prediction analysis.

Cite this Research Publication : M. Dharani, Malavika G. Prasad, Predicting shear modulus property using materials informatics, AIP Conference Proceedings, AIP Publishing, 2024, https://doi.org/10.1063/5.0228632

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