通过实施机器学习,通过超冷液体的空间和动态异质性的自动表征
1Ames Laboratory and Department of Chemistry, Iowa State University, Ames, IA, United States of America.
概括
本研究介绍了一种使用机器学习的计算方法,用于识别超冷液体中的不同区域. 这种方法揭示了这些区域,或纳米域,由于液态-液态相隔离而形成.
科学领域:
- 计算物理学的计算物理.
- 材料科学 是一种材料科学.
- 机器学习是机器学习.
背景情况:
- 超冷液体表现出复杂的结构和动态异质性.
- 了解这些异质性对于预测材料性质至关重要.
- 现有的方法往往难以准确地对这些液体内的动态区域进行分类.
研究的目的:
- 开发一种新的计算方法,用于识别超冷液体中的结构和动态异质性.
- 将粒子分类为不同的中位态,并将它们映射到现实空间中的纳米领域.
- 调查超冷液体中观察到的异质性的起源.
主要方法:
- 实施主要成分分析 (PCA) 结合K-means和高斯混合 (GM) 集群算法.
- 使用平均加权协调数 (WCNs) 作为减小维度的顺序参数.
- 采用一个共同学习策略,以代地改进结构和配置空间之间的粒子分类.
主要成果:
- 成功将粒子分类为稳定的中位态和相应的纳米域.
- 在这些已识别的领域内展示动态异质性.
- 观察到的异质性与超冷状态下液体-液体相分离的相关性.
结论:
- 开发的计算方法有效地识别和分类超冷液体中的异质性.
- 已识别的纳米域表现出明显的动态特性.
- 液-液相分离被确定为灭超冷液体中异质性的基本机制.
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