用各种合成方法制备的Fe-Co-Ni合金的机器学习预测的稳定性
Shakti P Padhy1,2, Soumya R Mishra1,3, Li Ping Tan1
1School of Materials Science and Engineering, Nanyang Technological University, Singapore 639798, Singapore.
iScience
|January 15, 2025
概括
机器学习确定了用于电磁设备的最佳Fe-Co-Ni合金组成. 实验结果验证了这些预测,显示弧样本的偏差小于14%,证实了ML框架.
科学领域:
- 材料科学 材料科学 材料科学
- 金工业是金工业的一个方面.
- 计算材料科学科学 计算材料科学
背景情况:
- 高性能合金对于先进的电磁能量转换至关重要.
- 之前的工作建立了用于合金设计的机器学习 (ML) 框架.
研究的目的:
- 通过ML框架预测Fe-Co-Ni合金组合物的实验验证.
- 研究合成方法对合金性能的影响.
主要方法:
- 使用了机器学习框架,使用多属性模型和贝叶斯优化.
- 合成了有前途的Fe-Co-Ni合金组合物,使用弧,SPS的球磨,以及SPS的化学合成.
- 实验性地描述了和磁化,库里温度和维克尔硬度.
主要成果:
- 弧融样本的实验性价值与ML预测的偏差不到14%.
- 对不同合成方法的结构变异进行了分析,以了解它们对性能的影响.
- 经过实验证实了ML预测的合金组合物的强度.
结论:
- 该研究验证了用于Fe-Co-Ni系统的ML驱动合金设计的有效性.
- 将加工条件整合到合金开发中可以提高预测准确性和材料性能.
- 这种方法加速了用于电磁应用的先进材料的发现.
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