当前位置: X-MOL 学术Nano Futures › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
The representative structure of graphene oxide nanoflakes from machine learning
Nano Futures ( IF 2.1 ) Pub Date : 2019-12-17 , DOI: 10.1088/2399-1984/ab58ac
Benyamin Motevalli , Amanda J Parker , Baichuan Sun , Amanda S Barnard

In this paper we revisit the structure of graphene oxide, and determine the pure and truly representative structures for graphene nanoflakes using machine learning. Using 20 396 random configurations relaxed at the electronic structure level, we observe the presence of hydroxyl, ether, double bonds, aliphatic (cyclohexane) disruption, defects and significant out-of-plane distortions that go beyond the Lerf–Klinowski model. Based on an diverse list of 224 chemical, structural and topological features we identify 25 archetypal ‘pure’ graphene oxide structures which capture all of the complexity and diversity of the entire data set; and three prototypes that are the truly representative averages in 224-dimensional space. Together these 28 structures, which are shown to be largely robust against changes in thermochemical conditions modeled using ab initio thermodynamics, can be downloaded and used collectively as a small data set for with a fraction of the computational cost ...

中文翻译:

机器学习中氧化石墨烯纳米薄片的代表性结构

在本文中,我们将重新审视氧化石墨烯的结构,并使用机器学习方法确定石墨烯纳米薄片的纯净且真正具有代表性的结构。使用在电子结构水平上松弛的20 396个随机构型,我们观察到存在羟基,醚,双键,脂族(环己烷)破坏,缺陷和明显的平面外扭曲,这些现象超出了Lerf-Klinowski模型。基于224种化学,结构和拓扑特征的多样化列表,我们确定了25种原型“纯”石墨烯氧化物结构,这些结构捕获了整个数据集的所有复杂性和多样性;和三个原型,它们是224维空间中真正具有代表性的平均值。结合这28个结构,
更新日期:2019-12-17
down
wechat
bug