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.

高熵合金（HEA）和成分复杂的合金（CCA）最近因其卓越的机械和物理性能而引起了极大的研究兴趣。尽管已经报道了许多有用的HEA或CCA，但是如果有相设计规则可以指导合金筛选，则仍然是一个未解决的问题。在这项工作中，我们对具有不同机器学习（ML）算法的学术界通常使用的现有设计规则进行了严格的评估。基于人工神经网络算法，我们能够从ML建模中提取出敏感性矩阵，从而能够定量评估如何调整设计参数以形成特定相，例如固溶体，金属间化合物，或无定形相。此外，我们探索了在ML建模的HEA或CCA中进行阶段设计时使用的扩展的新设计参数集（以前从未考虑过）。为了验证我们的ML指导设计规则，我们进行了各种实验，并从Fe-Cr-Ni-Zr-Cu系统中设计了一系列合金。我们的实验结果与我们的预测相当吻合，这表明基于ML的技术可能是未来HEA或CCA设计中的有用工具。