机器学习用于开发用于磁道交叉点的新材料
Atsufumi Hirohata1,2,3, Hiroki Koizumi1,4, Tufan Roy1,2
1Center for Science and Innovation in Spintronics, Tohoku University, Sendai, Japan.
概括
材料科学家正在使用机器学习来发现新的复杂合金. 这种方法加速了用于磁力和自旋电子应用的先进材料的开发,超越了传统方法.
科学领域:
- 材料科学 材料科学 材料科学
- 计算材料设计设计 计算材料设计
- 合金开发 合金开发
背景情况:
- 增加合金和化合物中组成元素的数量对于增强材料性能至关重要.
- 像-铁- (NdFeB) 和-铁- (CoFeB) 这样的三级合金在永久磁铁和自旋电子中至关重要.
- 多元素合金的复杂性需要先进的发现方法,超越手工勘探.
研究的目的:
- 为开发新型多元元素材料提出一个标准化的流程.
- 利用机器学习来预测有前途的合金候选者.
- 概述改善材料开发工作流程的要求.
主要方法:
- 使用机器学习算法来预测候选多元元素合金.
- 使用ab initio计算来选预测的候选人.
- 整合量子化来增强机器学习在材料开发中的采用.
主要成果:
- 确定加速材料发现的系统方法.
- 展示机器学习在预测复杂合金组成方面的能力.
- 突出计算方法和实验验证之间的协同作用.
结论:
- 集成机器学习,ab initio计算和量子化的标准化过程可以显著推进材料开发.
- 这种计算方法对于探索多元素合金的庞大组成空间至关重要.
- 这一过程的进一步完善将扩大机器学习在创建下一代材料中的应用性.
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