基于原子势描述符的晶体缺陷的无监督识别

IF 9.4 1区 材料科学 Q1 CHEMISTRY, PHYSICAL
Lukáš Kývala, Pablo Montero de Hijes, Christoph Dellago
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引用次数: 0

摘要

识别晶体缺陷对于揭示许多物理现象的起源至关重要。传统上使用的序参数是系统相关的,并且对于长分子动力学模拟的计算成本很高。无监督算法提供了一种独立于所研究系统的替代方法,并且可以利用分子动力学模拟中预先计算的原子势描述符。我们比较了三种这样的算法(PCA, UMAP和PaCMAP)在硅和水系统上的性能。首先,我们评估了识别相的算法,包括晶体多晶和熔体,然后扩展了我们的分析,以识别间隙、空位和界面。虽然发现PCA不适合有效分类,但它已被证明是UMAP和PaCMAP的合适初始化。总的来说,UMAP和PaCMAP都显示出令人满意的结果,PaCMAP在分类方面更健壮,除了在明显的类不平衡的情况下,UMAP表现得更好。值得注意的是,这两种算法都成功地识别了过冷水中的核,证明了它们对水中冰核的适用性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Unsupervised identification of crystal defects from atomistic potential descriptors

Unsupervised identification of crystal defects from atomistic potential descriptors

Identifying crystal defects is vital for unraveling the origins of many physical phenomena. Traditionally used order parameters are system-dependent and can be computationally expensive to calculate for long molecular dynamics simulations. Unsupervised algorithms offer an alternative independent of the studied system and can utilize precalculated atomistic potential descriptors from molecular dynamics simulations. We compare the performance of three such algorithms (PCA, UMAP, and PaCMAP) on silicon and water systems. Initially, we evaluate the algorithms for recognizing phases, including crystal polymorphs and the melt, followed by an extension of our analysis to identify interstitials, vacancies, and interfaces. While PCA is found unsuitable for effective classification, it has been shown to be a suitable initialization for UMAP and PaCMAP. Both UMAP and PaCMAP show promising results overall, with PaCMAP proving more robust in classification, except in cases of significant class imbalance, where UMAP performs better. Notably, both algorithms successfully identify nuclei in supercooled water, demonstrating their applicability to ice nucleation in water.

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来源期刊
npj Computational Materials
npj Computational Materials Mathematics-Modeling and Simulation
CiteScore
15.30
自引率
5.20%
发文量
229
审稿时长
6 weeks
期刊介绍: npj Computational Materials is a high-quality open access journal from Nature Research that publishes research papers applying computational approaches for the design of new materials and enhancing our understanding of existing ones. The journal also welcomes papers on new computational techniques and the refinement of current approaches that support these aims, as well as experimental papers that complement computational findings. Some key features of npj Computational Materials include a 2-year impact factor of 12.241 (2021), article downloads of 1,138,590 (2021), and a fast turnaround time of 11 days from submission to the first editorial decision. The journal is indexed in various databases and services, including Chemical Abstracts Service (ACS), Astrophysics Data System (ADS), Current Contents/Physical, Chemical and Earth Sciences, Journal Citation Reports/Science Edition, SCOPUS, EI Compendex, INSPEC, Google Scholar, SCImago, DOAJ, CNKI, and Science Citation Index Expanded (SCIE), among others.
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