DynStabNet: 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 phonon calculations at the inference stage. Rather than serving as a replacement for first-principles phonon calculations, DynStabNet is designed as a fast surrogate model to accelerate the prescreening of candidate structures prior to subsequent MLIP- or DFT-based phonon validation. Specifically, a crystal structure generation model is utilized to produce diverse candidates, and a pretrained machine learning potential is employed to rapidly compute their phonon spectra to construct the training data set. The E(3)-equivariant architecture enables DynStabNet to capture the complex relationships between crystal structures and their dynamical stability. Consequently, unstable configurations can be rapidly eliminated early in the materials design pipeline. The DynStabNet model achieves an accuracy of 97% while reducing the evaluation time per structure from several hours to approximately 1 ms. By dramatically accelerating the overall screening process, this approach provides an efficient framework for large-scale materials screening.