机械硬软磁高合金的数据驱动设计
Mian Dai1, Yixuan Zhang1, Xiaoqing Li2
1Institute of Materials Science, Technical University of Darmstadt, Alarich-Weiss-Str. 2, Darmstadt, Germany.
Advanced science (Weinheim, Baden-Wurttemberg, Germany)
|March 26, 2025
概括
本研究引入了一个数据驱动的框架,以发现具有机械硬度和磁性软度的新型高合金 (HEAs). 这种方法加速了用于技术应用的先进材料的设计.
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
- 合金设计设计 合金设计
背景情况:
- 开发具有高硬度和软磁性质的材料对于先进技术至关重要.
- 探索高合金 (HEA) 的庞大的组成空间是一个重大挑战.
研究的目的:
- 提出一个数据驱动的框架,用于发现多功能HEAs.
- 为了识别优化高硬度和磁性软度的HEA候选物.
主要方法:
- 利用了一大数据集,包括1,842,628个密度函数理论计算.
- 分析了由42个元素衍生出的四等和五等等质HEAs.
- 采用集体学习和预测模型来关联组成,结构和属性.
主要成果:
- 已建立的预测模型将合金组成与机械和磁性属性联系起来.
- 展示了一个有效探索HEA组合空间的框架.
- 确定了用于多功能应用的有前途的HEA候选者.
结论:
- 数据驱动的策略显著加快了先进材料的发现.
- 提出的框架允许设计下一代合金,具有所需的硬度和磁性软度.
- 强调了计算方法在材料创新的潜力.
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