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Meeting

2027 TMS Annual Meeting & Exhibition

Symposium

Computational Discovery and Design of Materials

Presentation Title

Understanding Point Defects in Semiconductors using First Principles Simulations Accelerated by ML Potentials

Author(s)

Arun Kumar Mannodi Kanakkithodi

On-Site Speaker (Planned)

Arun Kumar Mannodi Kanakkithodi

Abstract Scope

In this talk, I will discuss my group’s work in developing machine learning interatomic potential (MLIP) models for accelerating density functional theory (DFT) simulations of point defects in semiconductors. Using a multi-fidelity DFT dataset of native point defects, impurities, dopants, and defect complexes across a wide space of semiconductors, we trained (or fine-tuned) various foundation MLIPs such as MACE, UMA, and M3GNET, to subsequently enable charge-dependent prediction and optimization of thousands of new point defects and complexes. High-fidelity simulations were performed on the lowest energy defects to generate a final computational defect dataset. This scheme was applied for rational discovery and screening of low energy defect structures in dozens of semiconductors belonging to: (a) Cd/Zn-Te/Se/S compositions, (b) a variety of halide (e.g., CsPbI3) and chalcogenide (e.g., BaZrS3) perovskites, (c) zincblende-derived ternary and quaternary chalcogenides (e.g., Cu(In,Ga)S2 and Ag2ZnSnSe4), and (d) various wide band gap oxides (e.g., B2O3 and HfGeO4).

Proceedings Inclusion?

Planned:

Keywords

Computational Materials Science & Engineering, Machine Learning, Modeling and Simulation

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Understanding Point Defects in Semiconductors using First Principles Simulations Accelerated by ML Potentials

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