高熵合金自2004年问世以来，因其卓越的机械和物理性能引起广泛的研究兴趣。与传统合金相比，高熵合金通常包含五种以上元素,势能面比较复杂，潜在的亚稳结构较多，形成哪一种亚稳结构通常取决于具体的实验条件。由于这些复杂性，预测高熵合金的相结构至今仍是一个难题。
来自中国香港城市大学工程学院力学工程系的杨勇团队，基于人工神经网络等三种不同的算法开发了机器学习模型，用于指导高熵合金的相结构设计。他们首先采用了一组包含601个多元合金数据的数据集训练了模型，之后基于该模型定量评估了文献中已有的高熵合金相结构的设计规则，并探索提出了一组全新的设计参数。这些新参数与多组元系统的势能面波动相关联，因此大大提高了机器学习模型的准确性。为了验证模型的可靠性，他们基于Fe-Cr-Ni-Zr-Cu多元体系开展了一系列实验，包括铸造、熔融纺丝和共溅射等，并设计出了一系列新型合金，实验结果与理论预测高度吻合。该研究表明，基于机器学习技术有望发展高熵或多组元合金设计的新工具。
该文近期发表于npj Computational Materials 5: 128 (2019)，英文标题与摘要如下，点击https://www.nature.com/articles/s41524-019-0265-1可以自由获取论文PDF。
Machine learning guided appraisal and exploration of phase design for high entropy alloys
Ziqing Zhou, Yeju Zhou, Quanfeng He, Zhaoyi Ding, Fucheng Li & Yong Yang
High entropy alloys (HEAs) and compositionally complex alloys (CCAs) have recently attracted great research interest because of their remarkable mechanical and physical properties. Although many useful HEAs or CCAs were reported, the rules of phase design, if there are any, which could guide alloy screening are still an open issue. In this work, we made a critical appraisal of the existing design rules commonly used by the academic community with different machine learning (ML) algorithms. Based on the artificial neural network algorithm, we were able to derive and extract a sensitivity matrix from the ML modeling, which enabled the quantitative assessment of how to tune a design parameter for the formation of a certain phase, such as solid solution, intermetallic, or amorphous phase.Furthermore, we explored the use of an extended set of new design parameters, which had not been considered before, for phase design in HEAs or CCAs with the ML modeling.To verify our ML-guided design rule, we performed various experiments and designed a series of alloys out of the Fe-Cr-Ni-Zr-Cu system. The outcomes of our experiments agree reasonably well with our predictions, which suggests that the ML-based techniques could be a useful tool in the future design of HEAs or CCAs.
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