arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 1901.08754cond-mat.mtrl-sci
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Cluster-mining:一种从原子对分布函数数据确定金属纳米颗粒核结构的方法

Cluster-mining: An approach for determining core structures of metallic nanoparticles from atomic pair distribution function data

Soham Banerjee, Chia-Hao Liu, Kirsten M. O. Jensen, Pavol Juhas, Jennifer D. Lee, Marcus Tofanelli, Christopher J. Ackerson, Christopher B. Murray, Simon J. L. Billinge

摘要:

我们提出了一种新的方法来寻找和评估小金属纳米粒子的结构模型。该方法不是拟合具有许多自由度的单个模型,而是通过算法从多个结构基序构建纳米颗粒簇库,并将其单独拟合到实验PDF。每个聚类拟合都受到高度约束。该方法称为聚类挖掘,返回与数据一致的所有候选结构模型,这些模型由拟合优度测量。它是高度自动化的,易于使用,并产生的模型,是更物理上的现实,并导致更好的协议的数据比模型的基础上立方紧密堆积的结晶核心,经常在文献中报道的金属纳米粒子。

英文摘要:

We present a novel approach for finding and evaluating structural models of small metallic nanoparticles. Rather than fitting a single model with many degrees of freedom, the approach algorithmically builds libraries of nanoparticle clusters from multiple structural motifs, and individually fits them to experimental PDFs. Each cluster-fit is highly constrained. The approach, called cluster-mining, returns all candidate structure models that are consistent with the data as measured by a goodness of fit. It is highly automated, easy to use, and yields models that are more physically realistic and result in better agreement to the data than models based on cubic close-packed crystallographic cores, often reported in the literature for metallic nanoparticles.

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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