纳米团簇
债券
计算机科学
粘结长度
度量(数据仓库)
功能(生物学)
人工智能
材料科学
纳米技术
物理
分子
量子力学
数据挖掘
进化生物学
生物
经济
财务
作者
Zhang, Xinxu,Jia, Hui,Tian, Fangzhen,Liu, Yandi,Carsten, Ullrich,Wu, Yulong,Liu, Changlong,Zhang, Xiaodong,Li, Yonghui
出处
期刊:Cornell University - arXiv
日期:2023-05-13
标识
DOI:10.48550/arxiv.2305.07942
摘要
Complicated S-Au interaction patterns in thiolate-protected gold nanoclusters (TP-AuNCs) are important in the formation of versatility among clusters. In this study, the ELFnet, a novel convolutional neural network (CNN) model is trained and tested to bridge the electron localization function (ELF) images and the bond lengths. As believed to be a successful bond describer, a dataset of 3959 ELF images is obtained out of Density Functional Theory (DFT) simulations, followed by a series of model architecture and hyperparameter exploration. The ELFnet with its best performance shows its correct feature extraction ability to gain insights in the prediction of bond lengths. Besides, the ELFnet also possess 3 different "ELF reading modes" which inspires other possibilities in understanding ELF images. The ELFnet serves as an "ELF-ruler" to give precise bond type labels (or the predicted bond lengths). Such ELF-ruler measured bond types represent a particular bond when distorted by external factors with a quantitative indicator. With the help of such an ELF-ruler, the classification of chemical bonds in general may be extended to more precise types.
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