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Meeting 2027 TMS Annual Meeting & Exhibition
Symposium AI/ML/Data Informatics for Materials Discovery: Bridging Experiment, Theory, and Modeling
Presentation Title Multiscale Modeling of GaN/AlGaN Interfaces with Machine Learning and DFT
Author(s) Daniel Wines, Viviana Faride Dovale Farelo, Ali Hamze, Lucas Hale, Brian DeCost
On-Site Speaker (Planned) Daniel Wines
Abstract Scope High-electron-mobility transistors (HEMTs) involving GaN/AlGaN interfaces are heavily impacted by interface defects that can modulate thermal transport and electronic properties. To address these effects across scales, we implement a multiscale modeling approach leveraging machine learning (ML) trained on DFT data. We utilize ML interatomic potentials (PACE, GRACE) to simulate realistic point and extended defects at the interface, computing properties such as thermal conductivity, which serve as input parameters for device-level simulations (TCAD). Simultaneously, we utilize the GEARS-H ML Hamiltonian model trained on DFT, which allows us to scale quantum simulations to thousands of atoms, enabling a detailed study of how interface morphology and defects impact the 2D electron gas (2DEG). This workflow demonstrates how ML can accelerate the multiscale modeling of high power electronics by linking atomic defects to device-scale behavior.
Proceedings Inclusion? Planned:
Keywords Computational Materials Science & Engineering, Modeling and Simulation, Machine Learning

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