Predicting the melting temperature (Tm) of multi-component and high-entropy alloys (HEAs) is critical for high-temperature applications but computationally expensive using traditional CALPHAD or DFT methods. In this work, we develop a gradient-boosted decision tree (XGBoost) model to predict Tm for complex alloys based on elemental properties. To ensure physical consistency, we address the issue of data leakage by excluding temperature-dependent thermodynamic descriptors (such as Gibbs free energy of mixing) and instead rely on physically motivated elemental features. The optimized model achieves a coefficient of determination (R2) of 0.948 and a Mean Squared Error (MSE) of 9928 which is about 5% relative error for HEAs on a validation set of approximately 1300 compositions. Crucially, we validate the model using the Valence Electron Concentration (VEC) rule. Without explicit constraints during training, the model successfully captures the known stability transition between BCC and FCC phases at a VEC of approximately 6.87. These results demonstrate that data-driven models, when properly feature-engineered, can capture fundamental metallurgical principles for rapid alloy screening.

翻译：预测多组分及高熵合金（HEAs）的熔点（Tm）对高温应用至关重要，但采用传统的CALPHAD或DFT方法计算成本高昂。本研究开发了一种梯度提升决策树（XGBoost）模型，基于元素性质预测复杂合金的熔点。为确保物理一致性，我们通过排除与温度相关的热力学描述符（如混合吉布斯自由能）来解决数据泄露问题，转而依赖具有物理意义的元素特征。优化后的模型在约1300种成分的验证集上，对高熵合金的预测决定系数（R²）达到0.948，均方误差（MSE）为9928，相对误差约为5%。关键的是，我们采用价电子浓度（VEC）规则验证了模型。在训练过程中未施加显式约束的情况下，该模型成功捕捉到价电子浓度约6.87处体心立方（BCC）与面心立方（FCC）相之间已知的稳定性转变。这些结果表明，经过恰当特征工程的数据驱动模型能够捕捉基本的冶金学原理，实现合金的快速筛选。