The phase structure information of high-entropy alloys (HEAs) is critical for their design and application, as different phase configurations are associated with distinct chemical and physical properties. However, the broad range of elements in HEAs presents significant challenges for precise experimental design and rational theoretical modeling and simulation. To address these challenges, machine learning (ML) methods have emerged as powerful tools for phase structure prediction. In this study, we use a dataset of 544 HEA configurations to predict phases, including 248 intermetallic, 131 solid solution, and 165 amorphous phases. To mitigate the limitations imposed by the small dataset size, we employ a Generative Adversarial Network (GAN) to augment the existing data. Our results show a significant improvement in model performance with data augmentation, achieving an average accuracy of 94.77% across ten random seeds. Validation on an independent dataset confirms the model's reliability and real-world applicability, achieving 100% prediction accuracy. We also predict FCC and BCC phases for SS HEAs based on elemental composition, achieving a peak accuracy of 98%. Furthermore, feature importance analysis identifies correlations between compositional features and phase formation tendencies, which are consistent with experimental observations. This work proposes an effective strategy to enhance the accuracy and generalizability of machine learning models in phase structure prediction, thus promoting the accelerated design of HEAs for a wide range of applications.

中文翻译：

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通过机器学习和数据增强增强高熵合金的相预测


高熵合金 （HEA） 的相结构信息对其设计和应用至关重要，因为不同的相构型与不同的化学和物理性质相关。然而，高熵合金中元件的广泛范围对精确的实验设计和合理的理论建模和模拟提出了重大挑战。为了应对这些挑战，机器学习 （ML） 方法已成为相结构预测的强大工具。在这项研究中，我们使用了 544 种高熵合金构型的数据集来预测相，包括 248 种金属间化合物、131 种固溶体和 165 种非晶相。为了减轻数据集规模小带来的限制，我们采用了生成对抗网络 （GAN） 来增强现有数据。我们的结果表明，数据增强后模型性能显著提高，10 个随机种子的平均准确率达到 94.77%。在独立数据集上进行验证可确认模型的可靠性和实际适用性，从而实现 100% 的预测准确性。我们还根据元素组成预测 SS HEA 的 FCC 和 BCC 相，实现了 98% 的峰值准确度。此外，特征重要性分析确定了成分特征和相形成趋势之间的相关性，这与实验观察结果一致。这项工作提出了一种有效的策略，以提高机器学习模型在相结构预测中的准确性和泛化性，从而促进高熵合金的加速设计，应用范围广泛。