用于高熵合金多组元多相微结构演化加速长时序预测的深度学习方法

中文摘要 AI 辅助

相场建模是预测微结构演化的有效方法，但对于大时空尺度下的多组元多相体系，其计算成本会变得过高。本研究提出一种AE-GCN-LSTM代理框架，用于含BCC与FCC相共存的多组元AlCrFeNi高熵合金体系微结构演化的长时序预测。该框架采用多头自编码器将四个元素浓度场和相场序参数压缩为隐表示，这些隐表示被构造成图以学习其时空演化规律。该框架可准确预测长达3,000,000个模拟时间步的微结构演化，且在未进行重新训练、微调或参数调整的情况下，于未见条件下对其鲁棒性进行了系统评估，评估内容包括FCC析出相的尺寸与初始位置变化、含1个、2个及5个FCC析出相的微结构，以及涉及析出相合并与分裂的复杂相相互作用。尽管仅在含单一名义合金成分的100×100计算域上训练，该框架仍成功迁移至更大的256×256和512×512体系，以及未见的AlCrFeNi合金成分。在所有评估配置中，该模型可保留主导相的形貌与成分演化，相比传统相场模拟提供约7200至62300倍的计算加速。这些结果表明，基于隐图的AE-GCN-LSTM预测方法为多组元多相微结构的长时序模拟提供了可扩展且计算高效的代理模型，为高通量合金设计提供了有前景的基础。

英文摘要

Phase-field modeling provides a powerful approach for predicting microstructure evolution but becomes computationally prohibitive for multicomponent and multiphase systems over large spatial and temporal scales. This work presents an AE-GCN-LSTM surrogate framework for long-horizon forecasting of microstructure evolution in the multicomponent AlCrFeNi high-entropy alloy system containing coexisting BCC and FCC phases. A multi-head autoencoder compresses the four elemental concentration fields and phase-field order parameter into latent representations, which are formulated as graphs for learning their spatial and temporal evolution. The framework accurately forecasts microstructure evolution over horizons extending to 3,000,000 simulation timesteps. Its robustness is systematically evaluated under previously unseen conditions without retraining, fine-tuning, or parameter adaptation. These evaluations include variations in FCC precipitate size and initial position, microstructures containing one, two, and five FCC precipitates, and complex phase interactions involving precipitate merging and splitting. Although trained only on 100 x 100 computational domains containing a single nominal alloy composition, the framework is successfully transferred to larger 256 x 256 and 512 x 512 systems and to previously unseen AlCrFeNi compositions. Across the evaluated configurations, the model preserves the dominant phase morphology and compositional evolution while providing computational speedups ranging from approximately 7200 to 62300 relative to conventional phase-field simulations. These results demonstrate that latent graph-based AE-GCN-LSTM forecasting provides a scalable and computationally efficient surrogate for long-horizon simulation of multicomponent, multiphase microstructures and offers a promising foundation for high-throughput alloy design.

发表机构

• KU Leuven（鲁汶大学）

机构由 AI 辅助整理，请以论文原文为准。