Eutectic alloys have garnered significant attention due to their promising mechanical and physical properties, as well as their technological relevance. However, the discovery of eutectic compositionally complex alloys (ECCAs) (e.g. high entropy eutectic alloys) remains a formidable challenge in the vast and intricate compositional space, primarily due to the absence of readily available phase diagrams. To address this issue, we have developed an explainable machine learning (ML) framework that integrates conditional variational autoencoder (CVAE) and artificial neutral network (ANN) models, enabling direct generation of ECCAs. To overcome the prevalent problem of data imbalance encountered in data-driven ECCA design, we have incorporated thermodynamics-derived data descriptors and employed K-means clustering methods for effective data pre-processing. Leveraging our ML framework, we have successfully discovered dual- or even tri-phased ECCAs, spanning from quaternary to senary alloy systems, which have not been previously reported in the literature. These findings hold great promise and indicate that our ML framework can play a pivotal role in accelerating the discovery of technologically significant ECCAs.

共晶合金由于其良好的机械和物理性能以及技术相关性而引起了广泛关注。然而，在广阔而复杂的成分空间中，共晶成分复杂合金（ECCA）（例如高熵共晶合金）的发现仍然是一个艰巨的挑战，这主要是由于缺乏容易获得的相图。为了解决这个问题，我们开发了一个可解释的机器学习（ML）框架，集成了条件变分自动编码器（CVAE）和人工神经网络（ANN）模型，从而能够直接生成ECCA。为了克服数据驱动的 ECCA 设计中普遍存在的数据不平衡问题，我们结合了热力学衍生的数据描述符，并采用 K 均值聚类方法进行有效的数据预处理。利用我们的机器学习框架，我们成功地发现了双相甚至三相 ECCA，涵盖从四元到六元合金系统，这在之前的文献中尚未报道过。这些发现前景广阔，表明我们的机器学习框架可以在加速发现具有技术意义的 ECCA 方面发挥关键作用。