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arXiv 2602.13337cond-mat.softphysics.comp-ph
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StrAPS: 结构角功率谱用于发现块共聚物中的新形态

StrAPS: Structural Angular Power Spectrum for Discovering Novel Morphologies in Block Copolymers

The University of Melbourne(墨尔本大学)

Dominic M. Robe, Elnaz Hajizadeh

AI总结:

StrAPS通过角功率谱分析块共聚物形态,实现自动识别新结构,无需预先定义可能形态。

摘要:

相分离系统的形态具有对称群等正式区别,但对特定相场进行标记的分析协议需要手动专业知识、任意阈值或已建立的签名。在本工作中,研究了三维结构因子的角功率谱是否能区分不同形态。通过粗粒分子动力学模拟生成相分离块共聚物的构型,计算三维结构因子。将包含主峰的结构因子值壳层隔离。该二维场在球面上分解为偶次多项式度数ℓ≤12的球谐模,然后进一步减少为旋转不变的角功率谱。发现这些低ℓ系数能稳健地区分不同形态。这种分析为自动标记新结构提供了一种工具,无需事先枚举可能的形态。

英文摘要:

The morphologies of phase separating systems have formal distinctions such as symmetry groups, but the analysis protocol for labeling a particular phase field with a morphology requires manual expertise, arbitrary thresholds, or established signatures. In this work, it is investigated if the angular power spectrum of the 3D structure factor can discriminate between morphologies. The 3D structure factor is computed on configurations of phase separating block copolymers generated by coarse-grained molecular dynamics simulations. The shell of structure factor values containing the primary peaks is isolated. This 2D field on a sphere is decomposed into spherical harmonic modes of even polynomial degree $\ell\le 12$, then further reduced to the rotationally invariant angular power spectrum. It is found that these few coefficients for low $\ell$ discriminate robustly between different morphologies. This analysis serves as an automatic tool for flagging novel structures, without a need to enumerate the plausible morphologies in advance.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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