用物理特征预测难熔高熵合金熔点，精度超94%。
Physically Consistent Machine Learning for Melting Temperature Prediction of Refractory High-Entropy Alloys
- 基于元素特性构建XGBoost模型，避免温度依赖特征导致的数据泄露。
- 在约1300种成分上实现R2=0.948，误差仅约5%。
- 无需显式约束，自动捕捉体心与面心相变的价电子规律。
预测多组元高熵合金（HEAs）的熔点（Tm）对高温应用至关重要，但传统CALPHAD或DFT方法计算成本高昂。本文采用梯度提升决策树（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，剔除了温度依赖的热力学描述符（如混合吉布斯自由能），转而使用物理解释性强的元素特征。优化后的模型在约1300种成分的验证集上达到决定系数R²=0.948，均方误差MSE=9928，相对误差约为5%。关键的是，通过价电子浓度（VEC）规则进行验证：模型在未施加显式约束的情况下，成功捕获了体心立方（BCC）与面心立方（FCC）相变发生在VEC≈6.87处的已知稳定性转变。结果表明，经合理特征工程的数据驱动模型可有效捕捉基本冶金规律，适用于快速合金筛选。
原文摘要 · Abstract (English)
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.
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