Understanding and predicting microstructure evolution is fundamental to materials science, as it governs the resulting properties and performance of materials. Traditional simulation methods, such as phase-field models, offer high-fidelity results but are computationally expensive due to the need to solve complex partial differential equations at fine spatiotemporal resolutions. To address this challenge, we propose a deep learning-based framework that accelerates microstructure evolution predictions while maintaining high accuracy. Our approach utilizes a fully convolutional spatiotemporal model trained in a self-supervised manner using sequential images generated from simulations of microstructural processes, including grain growth and spinodal decomposition. The trained neural network effectively learns the underlying physical dynamics and can accurately capture both short-term local behaviors and long-term statistical properties of evolving microstructures, while also demonstrating generalization to unseen spatiotemporal domains and variations in configuration and material parameters. Compared to recurrent neural architectures, our model achieves state-of-the-art predictive performance with significantly reduced computational cost in both training and inference. This work establishes a robust baseline for spatiotemporal learning in materials science and offers a scalable, data-driven alternative for fast and reliable microstructure simulations.

翻译：理解与预测微观结构演化是材料科学的基础，因为它决定了材料的最终性能与表现。传统模拟方法（如相场模型）能够提供高保真结果，但由于需要在精细时空分辨率下求解复杂的偏微分方程，计算成本高昂。为应对这一挑战，我们提出一种基于深度学习的框架，在保持高精度的同时加速微观结构演化预测。我们的方法采用全卷积时空模型，通过使用微观结构过程（包括晶粒生长和旋节分解）模拟生成的序列图像进行自监督训练。训练后的神经网络能有效学习潜在的物理动力学，精确捕捉演化微观结构的短期局部行为与长期统计特性，同时展现出对未见时空域以及构型与材料参数变化的泛化能力。与循环神经网络架构相比，我们的模型以显著降低的训练和推理计算成本实现了最先进的预测性能。本研究为材料科学中的时空学习建立了稳健的基准，并为快速可靠的微观结构模拟提供了一种可扩展的数据驱动替代方案。