以旋转信息为基础的通用图形神经网络,用于模拟磁性排序
Wenbin Xu1,2, Rohan Yuri Sanspeur3, Adeesh Kolluru3
1National Energy Research Scientific Computing Center, Berkeley, CA 94720.
概括
我们开发了一个旋转信息图形神经网络,以加速磁性材料的发现. 这一框架改善了初始磁矩预测,加快了计算速度,并实现了准确的基态顺序确定.
科学领域:
- 材料科学 材料科学 材料科学
- 计算物理 计算物理
- 机器学习 机器学习
背景情况:
- 使用密度功能理论 (DFT) 发现磁性材料在计算上是昂贵的.
- 目前的通用机器学习原子间潜力 (uMLIP) 缺乏磁性排序预测能力.
- 现有的方法在确定基态磁性配置的高计算成本下扎.
研究的目的:
- 开发一种机器学习框架,以高效预测磁顺序.
- 扩展通用机器学习原子间潜能 (uMLIPs) 的功能,包括磁性特性.
- 加速对新型磁性材料的选和发现.
主要方法:
- 开发了一个数据效率高,自旋信息化的图形神经网络框架.
- 该框架包含旋转自由度,并保持物理对称性.
- 为数据集增强,实施了闭环异常检测方法.
主要成果:
- 该框架通过提供更好的磁矩初始猜测,显著加快密度函数理论 (DFT) 的计算速度.
- 在散装材料中精确地确定了地面状态的磁性排序.
- 该模型展示了用于预测表面磁性排序的概括能力.
结论:
- 开发的框架增强了UMLIP在磁性材料研究中的实用性.
- 这种方法加速了磁性材料的计算发现.
- 异常检测可以提高材料科学中的机器学习模型的准确性和稳定性.
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