机器学习预测和实验探索稀土合金的液态密度和粘度
1School of Physical Science and Technology, Northwestern Polytechnical University, Xi'an 710072, China.
The Journal of chemical physics
|October 24, 2025
概括
机器学习模型准确地预测稀土合金的液态密度和粘度. 支持向量机显示了最高的准确性,验证了Fe-Nd-B合金的实验测量.
科学领域:
- 材料科学 材料科学 材料科学
- 计算材料科学科学 计算材料科学
- 热物理性能 热物理性能
背景情况:
- 液态密度和粘度对于计算材料科学至关重要.
- 在高温,化学活性稀土合金中测量这些特性具有挑战性.
- 目前对Fe-Nd-B,Fe-Dy-B和Fe-Tb-B合金的数据有限.
研究的目的:
- 开发精确的预测模型用于稀土合金在液态中的热物理性质.
- 研究特定Fe-Nd-B基合金中密度和粘度的温度依赖性.
- 通过实验测量来验证机器学习预测.
主要方法:
- 使用随机森林,支持矢量机 (SVM) 和深度神经网络算法的预测模型的开发.
- 关于Fe-Nd-B,Fe-Dy-B和Fe-Tb-B合金测量热物理性质数据集的培训模型.
- 实验测量密度和粘度使用静电和电磁悬浮技术对特定合金.
主要成果:
- 支持向量机器模型实现了最高的预测准确度 (密度为0.973,粘度为0.986).
- 液体Fe76Nd5Tb3B16和Fe78Nd10Dy3Tb3B6合金的实验数据显示与SVM模型预测的高度一致 (R2值为0.817和0.921).
- 机器学习模型在预测稀土合金的液态特性方面表现出很高的准确性.
结论:
- 机器学习,特别是SVM,提供了一个非常准确的方法来预测稀土合金在液态中的热物理性质.
- 开发的模型可以克服与测量这些特性相关的实验困难.
- 这种方法促进了对高温合金的计算材料科学研究.
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