磁合金的基于DFT的磁性机器学习潜力受到约束:Fe-Al的案例研究
Alexey S Kotykhov1,2, Konstantin Gubaev3, Max Hodapp4
1Skolkovo Institute of Science and Technology, Skolkovo Innovation Center, Bolshoy Boulevard 30, Moscow, 143026, Russian Federation.
Scientific reports
|November 13, 2023
概括
我们开发了一种机器学习的原子间潜力,其中包括磁时刻,可以准确地预测激发状态下的磁性材料. 该方法准确地模拟复杂的磁性材料,包括铁合金.
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
- 凝聚物质物理学 凝聚物质物理学
背景情况:
- 对磁性材料的准确建模对于开发新技术至关重要.
- 传统的方法很难捕捉具有非平衡磁矩的材料的行为.
- 机器学习为开发更复杂的原子间潜能提供了一个有希望的途径.
研究的目的:
- 为多元组件磁性材料开发一种新的机器学习原子间潜力.
- 为了将磁时刻作为自由度与原子属性一起结合起来.
- 为了能够预测激发磁状态下的材料属性.
主要方法:
- 开发了一种机器学习的原子间潜力,考虑磁时刻,原子位置,类型和格子向量.
- 使用受约束密度函数理论 (cDFT) 创建了一个训练数据集,用于具有非平衡磁矩的配置.
- 接受过培训并验证了体中心立方体 (bcc) 铁 (Fe-Al) 合金的潜力,其组成和原子排列各不相同.
主要成果:
- 机器学习潜能准确地预测了Fe-Al系统的形成能量,格子参数和总磁矩.
- 计算的属性与从DFT获得的结果有很好的一致性.
- 该模型有质地复制了Fe-Al合金中实验观察到的异常体积组成依赖性.
结论:
- 拟议的机器学习原子间潜力有效地捕捉了磁性材料的行为,包括那些处于激发状态的材料.
- 这种方法为预测复杂磁性材料的性能提供了可靠的工具.
- 这些发现为加速发现和设计新型磁性材料铺平了道路.
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