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Updated: Jul 17, 2025

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Spatial Separation of Molecular Conformers and Clusters
Published on: January 9, 2014
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促进区块稀疏性的算法,用于高效地构建多元组合金的集群扩展模型.
Krishnamohan Thekkepat1,2,3, Sumanjit Das4, Debi Prosad Dogra4
1Indo-Korea Science and Technology Center, Jakkur, Bangalore 560065, India.
Journal of physics. Condensed matter : an Institute of Physics journal
|September 2, 2023
概括
一个新的算法有效地构建了多元组件合金的集群扩张 (CE) 模型. 这种方法显著减少了训练数据,并加快了配置采样,使得材料的发现速度更快.
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
- 合金理论 合金理论
背景情况:
- 多元件合金对于先进的结构和储能应用至关重要.
- 多组件合金的复杂性阻碍了传统的第一原则计算建模.
- 使用机器学习的集群扩展 (CE) 方法是建模合金乱的关键工具.
研究的目的:
- 介绍一种基于压力传感的新算法,用于高效的CE哈密尔顿构造在多元件合金中.
- 从最小的培训数据开发一种方法来创建稀疏的,从物理上有意义的CE模型.
- 为了加快多元件合金热力学的计算建模.
主要方法:
- 开发了一种压缩感应算法,用于构建稀疏的CE哈密尔顿.
- 使用了一个小的,精心挑选的合金结构训练集.
- 将算法应用于四种不同的合金系统:Ag-Au,Ag-Au-Cu,Ag-Au-Cu-Pd和 (Ge,Sn) ((S,Se,Te).
主要成果:
- 与传统方法相比,训练集大小减少了50%以上.
- 生成稀疏的CE模型,使配置空间采样至少快3倍.
- 成功地复制了已知的基态顺序和测试合金的顺序-失序过渡.
结论:
- 开发的算法大大降低了构建多元合金CE模型的成本和时间.
- 这种方法促进了多元组件合金的高通量计算选.
- 能够更有效地探索合金相位图和材料性能.
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