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Meeting

2026 TMS Annual Meeting & Exhibition

Symposium

AI/ML/Data Informatics for Materials Discovery: Bridging Experiment, Theory, and Modeling

Presentation Title

Elucidation of the Effects of Doping on the Bandgap of Bilayer Graphene using Interpretable Machine Learning

Author(s)

Rasika Jayarathna, Jagrity Chaudhary, Ahmed Al-Ostaz, Sasan Nouranian, Samrat Choudhury

On-Site Speaker (Planned)

Samrat Choudhury

Abstract Scope

Chemical doping of bilayer graphene has gained significant attention over the last decade due to its ability to control the bandgap, paving the way for next-generation materials. The bandgap of bilayer graphene in presence of defects and dopants is governed by a number of complex geometric and chemical descriptors making it difficult to navigate the vast search space using traditional electronic structure calculations. In this study, we attempt to elucidate the effects of double-layer doping on the bandgap using a combination of machine learning tools and density functional theory calculations. We focused on extracting the underlying physics leading to the formation of band gaps in bilayer graphene using explainable artificial intelligence tools such as symbolic regression. Later, more sophisticated machine learning tools such as physics-informed machine learning was utilized to predict the band gap in bilayer graphene with higher fidelity and less data in a computationally efficient manner.

Proceedings Inclusion?

Planned:

Keywords

Computational Materials Science & Engineering, Machine Learning, Electronic Materials

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