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复杂值K-Means集群的交互可分离密度适配算法大规模的混合功能启用 Ab Initio分子动力学模拟在平面波内
Shizhe Jiao1, Jielan Li1, Xinming Qin1
1Hefei National Research Center for Physical Sciences at the Microscale, and Anhui Center for Applied Mathematics, University of Science and Technology of China, Hefei, Anhui 230026, China.
The journal of physical chemistry. A
|March 2, 2024
概括
本研究引入了一种改进的K-平均集群方法,用于复杂值电子结构计算. 这种新方法提高了混合原始分子动力学模拟的准确性和稳定性,使得更大规模的计算成为可能.
科学领域:
- 计算化学的计算化学
- 材料科学 材料科学 材料科学
- 机器学习 机器学习
背景情况:
- 在电子结构计算中,K-means集群对于互极可分离密度适配 (ISDF) 是至关重要的.
- 使用实值K-平均值的现有方法是有效的,但对于复杂值轨道是有限的.
- 加快大规模的混合动力初始分子动力学 (混合AIMD) 模拟是至关重要的.
研究的目的:
- 在混合AIMD模拟中适应K-平均集群用于复杂值的Kohn-Sham轨道.
- 为了提高ISDF分解的准确性和效率.
- 为了实现更大,更复杂的分子动力学模拟.
主要方法:
- 建议使用复杂值轨道的平方模量之和来进行K-平均集群的改进权重函数.
- 应用了增强的K-means算法在混合AIMD中的ISDF分解.
- 实现了一个大规模并行版本用于大规模模拟.
主要成果:
- 新的重量函数产生了更顺的插值点,导致更稳定的能量潜力和更长的模拟时间步骤.
- 在液态水中实现了更精确的氧-氧辐射分布功能,并在二氧化中改善了功率光谱.
- 证明了超级计算机上数千个原子的模拟的可扩展性.
结论:
- 改进的K-means集群方法有效地处理混合AIMD中的复杂值轨道.
- 这一进步加速了大规模的电子结构计算,并提高了准确性.
- 并行实施有助于模拟以前无法实现的复杂系统.
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