High pressure solution treatment, followed by ambient pressure aging treatment, may serve as a powerful tool for enhancing the alloy properties by tailoring plenty of nanoscale precipitates. However, no theoretical descriptions of the microstructure evolution and prediction of mechanical properties during high pressure heat treatment (HPHT) exist. In this work, a novel atomic mobility model for binary system under pressure was first developed in the framework of CALculation of PHAse Diagram (CALPHAD) approach and applied to assess the pressure-dependent atomic mobilities of (Al) phase in the Al-Si system. Then, quantitative simulation of particle dissolution and precipitation growth for HPHT Al-Si alloys was achieved through the CALPHAD tools by coupling the present pressure-dependent atomic mobilities together with previously established thermodynamic descriptions. Finally, the relationship among composition, process, microstructure, and properties was constructed by combining the CALPHAD and machine learning methods to predict the hardness values for HPHT Al-Si alloys over a wide range of compositions and processes with limited experimental data. This work contributes to realizing the quantitative simulation of microstructure evolution and accurate prediction of mechanical properties in HPHT alloys and illustrates pathways to accelerate the discovery of advanced alloys.

中文翻译：

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一种新型合金在压力下的原子迁移率模型及其在高压热处理 Al-Si 合金中的应用（通过集成 CALPHAD 和机器学习）


高压固溶处理，然后是常压时效处理，可以作为一种强大的工具，通过定制大量的纳米级沉淀物来提高合金性能。然而，目前尚无关于高压热处理 （HPHT） 过程中微观结构演变和机械性能预测的理论描述。在这项工作中，首先在 PHAse 图 CALculation of PHAse Diagram （CALPHAD） 方法的框架下开发了一种新的二元系统原子迁移率模型，并应用于评估 Al-Si 系统中 （Al） 相的压力依赖性原子迁移率。然后，通过 CALPHAD 工具将当前的压力依赖性原子迁移率与先前建立的热力学描述耦合，实现了 HPHT Al-Si 合金颗粒溶解和沉淀增长的定量模拟。最后，通过结合 CALPHAD 和机器学习方法构建成分、工艺、微观组织和性能之间的关系，以预测高温高压 Al-Si 合金在有限实验数据下在各种成分和工艺下的硬度值。这项工作有助于实现 HPHT 合金微观组织演变的定量模拟和机械性能的准确预测，并阐明了加速发现先进合金的途径。