aceDynStabNet: A Deep Learning Framework for Fast Dynamical Stability Prediction of Crystal Structures
Semiconductor materials are widely used in electronic, optoelectronic, and energy applications. While DFT-based phonon calculations provide highly accurate assessments for dynamical stability of structures, their prohibitive computational cost poses a significant bottleneck for large-scale materials screening. Herein, we develop DynStabNet, an E(3)-equivariant graph neural network (E3GNN) framework that learns dynamical stability from phonon-informed data, enabling rapid prediction without the need for explicit…
DynStabNet E(3)-equivariant graph neural network predicts dynamical stability of semiconductor crystal candidates without explicit phonon calculations at inference, achieving 97% accuracy and cutting per-structure evaluation from hours to ~1 ms to accelerate large-scale screening.
Model is explicitly not a replacement for first-principles phonon calculations and is designed only for prescreening, requiring subsequent MLIP- or DFT-based validation.
Evidence
- Peer-reviewedThe Journal of Physical Chemistry Letters2026-07-21
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Truvace Impact Record TRV-2026-0510, v1: “DynStabNet: A Deep Learning Framework for Fast Dynamical Stability Prediction of Crystal Structures.” Truvace, 2026-07-22. /record/TRV-2026-0510 (accessed at citation time). sha256 9286f87b049caea1…
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