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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

High-Throughput Study of Amorphous Dielectric Materials: Integrating Foundation Potentials with Density Functional Theory

Author(s)

Daniel Wines, Kamal Choudhary

On-Site Speaker (Planned)

Daniel Wines

Abstract Scope

Amorphous dielectric materials are crucial for the microelectronics industry. The inherent lack of long-range order in amorphous structures create challenges for atomistic simulations. While ab initio Molecular Dynamics (MD) can accurately simulate amorphous materials, it is computationally demanding. To circumvent this computational cost, newly developed foundation potentials can be used in place of ab initio MD to obtain amorphous structures from the corresponding crystalline structures using melt/quench simulations. In this work, we created a database of over 5,000 amorphous materials using the MatterSim foundation potential. From these structures, we screened candidates based on dielectric properties using the Atomistic Line Graph Neural Network (ALIGNN) model and verified the most promising candidates with density functional theory (DFT). This work demonstrates how universal machine learning interatomic potentials can be coupled with DFT to accelerate the screening of amorphous dielectric materials.

Proceedings Inclusion?

Planned:

Keywords

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

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