用深度学习加速材料微观结构演化预测，仅需少量训练数据即可泛化到新成分。
Deep Learning-Driven Prediction of Microstructure Evolution via Latent Space Interpolation
- 通过条件变分自编码器学习微观结构的紧凑潜在表征。
- 在潜在空间用三次样条插值预测任意成分的微观结构演化。
- 结合球面线性插值实现平滑连续的形貌演化，逼近真实退火过程。
相场模型能精确模拟微观结构演化，但求解复杂微分方程导致计算成本高昂。本文提出一种基于深度学习的新框架，利用条件变分自编码器（CVAE）结合三次样条插值与球面线性插值（SLERP），显著加速演化预测。以二元旋节分解为例，仅需有限训练成分的相场模拟结果，即能通过训练好的CVAE学习到编码关键形貌特征的紧凑潜在表示。随后在潜在空间使用三次样条插值，预测任意未知成分的微观结构演化；最后通过SLERP保证随时间演化的形态变化平滑自然，近似于粗化过程。预测结果在视觉和统计上均与相场模拟高度一致。该框架为微观结构演化提供可扩展、高效的代理模型，助力材料设计与成分优化加速。
原文摘要 · Abstract (English)
Phase-field models accurately simulate microstructure evolution, but their dependence on solving complex differential equations makes them computationally expensive. This work achieves a significant acceleration via a novel deep learning-based framework, utilizing a Conditional Variational Autoencoder (CVAE) coupled with Cubic Spline Interpolation and Spherical Linear Interpolation (SLERP). We demonstrate the method for binary spinodal decomposition by predicting microstructure evolution for intermediate alloy compositions from a limited set of training compositions. First, using microstructures from phase-field simulations of binary spinodal decomposition, we train the CVAE, which learns compact latent representations that encode essential morphological features. Next, we use cubic spline interpolation in the latent space to predict microstructures for any unknown composition. Finally, SLERP ensures smooth morphological evolution with time that closely resembles coarsening. The predicted microstructures exhibit high visual and statistical similarity to phase-field simulations. This framework offers a scalable and efficient surrogate model for microstructure evolution, enabling accelerated materials design and composition optimization.
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