使用机器学习寻找稳定和低能量的Ce-Co-Cu三元化合物
Weiyi Xia1,2, Wei-Shen Tee1,2, Paul Canfield1,2
1Ames National Laboratory, U.S. Department of Energy, Iowa State University, Ames, Iowa 50011, United States.
Inorganic chemistry
|May 9, 2025
概括
研究人员使用机器学习和第一原则计算来发现新的基磁性材料. 这加快了对具有高磁化潜力的新型永久磁体的搜索.
科学领域:
- 材料科学 材料科学 材料科学
- 凝聚物质物理学 凝聚物质物理学
- 计算材料科学科学 计算材料科学
背景情况:
- 基于的金属间材料是先进永久磁铁的有希望的候选者.
- 开发新的磁性材料需要有效地探索复杂的组成空间.
研究的目的:
- 为了研究Ce-Co-Cu三元系统的新型永久磁铁材料.
- 用计算方法加速发现稳定和高性能磁性化合物.
主要方法:
- 使用了机器学习框架,特别是晶体图卷积神经网络 (CGCNN).
- 集成的CGCNN与第一原则计算,以实现高效的材料选.
- 预测候选化合物的热力学稳定性和动态性质.
主要成果:
- 确定了五种稳定的Ce-Co-Cu化合物,其形成能量低于凸起的船体.
- 发现了数百种低能量,潜在的转移稳定的Ce-Co-Cu三元化合物.
- 在两个富含的化合物中预测的高磁性:Ce4Co33Cu和Ce4Co31Cu3.
结论:
- 这种由ML指导的方法显著加速了新磁性材料的发现.
- Ce-Co-Cu系统为下一代永久磁铁提供了有前途的候选人.
- 计算方法对于导航复杂材料景观和识别高性能化合物至关重要.
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