Reproducible capillary fluctuation analysis of solid-liquid interfaces for stiffness and anisotropy calculations

IF 13.1 1区 材料科学 Q1 CHEMISTRY, PHYSICAL
Kai Liu, Douglas E. Spearot, Damien Tourret
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引用次数: 0

Abstract

The capillary fluctuation method (CFM) is widely used to compute solid–liquid interfacial properties from atomistic simulations, but its accuracy depends on choices in interface construction, wave-vector selection, sampling, and simulation geometry. Here, we develop a diagnostics-driven workflow for reproducible CFM calculations using pure Al as a representative system. Employing both ribbon models and thick two-dimensional references, we show that the apparent linearity of the fluctuation spectrum alone does not guarantee reliable estimates of stiffness or anisotropy. Instead, a reliable CFM analysis requires a fitting window consistent with both temporal sampling and continuum capillary-wave assumptions, systematic sensitivity tests of the interface identification procedure, explicit propagation of replica variability, and independent verification of model-thickness convergence. We further propose a practical thickness-selection rule based on coexistence-temperature consistency, which enables control of finite-size effects while retaining the substantial computational efficiency of ribbon geometries. By making the main sources of uncertainty explicit and diagnosable, the proposed workflow improves the reliability of the CFM as a quantitative tool and provides a foundation for its broader application to complex solid–liquid interfaces.
固液界面刚度和各向异性计算的可重复毛细管波动分析
毛细管波动法(CFM)被广泛用于原子模拟计算固液界面性质,但其精度取决于界面结构、波矢量选择、采样和模拟几何形状的选择。在这里,我们开发了一个诊断驱动的工作流,用于使用纯人工智能作为代表性系统进行可重复的CFM计算。采用带状模型和厚二维参考,我们表明波动谱的明显线性本身并不能保证可靠的刚度或各向异性估计。相反,可靠的CFM分析需要一个与时间采样和连续毛细管波假设相一致的拟合窗口,界面识别过程的系统灵敏度测试,副本可变性的显式传播,以及模型厚度收敛的独立验证。我们进一步提出了一种实用的基于共存温度一致性的厚度选择规则,该规则可以控制有限尺寸效应,同时保持条带几何形状的大量计算效率。通过明确和可诊断的主要不确定性来源,所提出的工作流程提高了CFM作为定量工具的可靠性,并为其在复杂固液界面的广泛应用奠定了基础。
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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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