The thermoelastic properties of monolayer covalent organic frameworks studied by machine-learning molecular dynamics

热弹性阻尼 单层 材料科学 分子动力学 共价键 动力学(音乐) 热的 化学物理 纳米技术 热力学 计算化学 物理 化学 有机化学 声学
作者
Bing Wang,Penghua Ying,Jin Zhang
出处
期刊:Nanoscale [Royal Society of Chemistry]
卷期号:16 (1): 237-248 被引量:8
标识
DOI:10.1039/d3nr04509a
摘要

Two-dimensional (2D) covalent organic frameworks (COFs) are emerging as promising 2D polymeric materials with broad applications owing to their unique properties, among which the mechanical properties are quite important for various applications. However, the mechanical properties of 2D COFs have not been systematically studied yet. Herein, a machine-learned neuroevolution potential (NEP) was developed to study the elastic properties of two representative monolayer 2D COFs, namely COF-1 and COF-5. The trained NEP enables one to study the elastic properties of 2D COFs in realistic situations (e.g., finite size and temperature) and possesses greatly improved computational efficiency when compared with density functional theory calculations. With the aid of the obtained NEP, molecular dynamics (MD) simulations together with a strain-fluctuation method were employed to evaluate the elastic constants of the considered 2D COFs at different temperatures. The elastic constants of COF-1 and COF-5 monolayers were found to decrease with an increase in the temperature, though they were almost isotropic irrespective of the temperature. The thermally induced softening of 2D COFs below a critical temperature was observed, which is mainly attributed to their inherent ripple configurations at finite temperatures, while above the critical temperature, the damping effect of anharmonic vibrations became the dominant factor. Based on the proposed mechanisms, analytical models were developed for capturing the temperature dependence of elastic constants, which were found to agree with the MD simulation results well. This work provides an in-depth insight into the thermoelastic properties of monolayer COFs, which can guide the development of 2D COF materials with tailored mechanical behaviors for enhancing their performance in various applications.

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
多肉丸子发布了新的文献求助10
刚刚
1秒前
天天快乐应助小小眼采纳,获得10
1秒前
1秒前
suhang2024完成签到 ,获得积分10
1秒前
fasiofafew发布了新的文献求助10
2秒前
2秒前
翎翎给翎翎的求助进行了留言
2秒前
材料化学左亚坤完成签到,获得积分10
2秒前
幽默书白发布了新的文献求助10
3秒前
3秒前
肖战战完成签到 ,获得积分10
4秒前
董宇峰发布了新的文献求助10
4秒前
天开眼完成签到,获得积分10
4秒前
OKOK发布了新的文献求助10
5秒前
ding应助二宝采纳,获得10
5秒前
上官若男应助半点心采纳,获得10
6秒前
ljf123456完成签到,获得积分10
6秒前
我的文献完成签到,获得积分10
6秒前
shine完成签到,获得积分10
6秒前
17876581310发布了新的文献求助10
6秒前
6秒前
科研通AI6.1应助yehuitao采纳,获得10
7秒前
7秒前
汤圆完成签到,获得积分10
7秒前
充电宝应助胡萝卜采纳,获得10
8秒前
8秒前
星辰大海应助迷路擎苍采纳,获得10
9秒前
tt关注了科研通微信公众号
9秒前
yqb发布了新的文献求助10
10秒前
Ava应助做好自己采纳,获得10
11秒前
Orange应助vogo7采纳,获得10
11秒前
我是老大应助独特的老四采纳,获得10
11秒前
2282521792完成签到,获得积分20
11秒前
12秒前
英俊的铭应助17876581310采纳,获得10
13秒前
13秒前
充电宝应助OKOK采纳,获得10
13秒前
三生有幸发布了新的文献求助10
14秒前
15秒前
高分求助中
Adhesion Science: Principles & Practice 1234
Signals, Systems, and Signal Processing 610
Burger's Medicinal Chemistry and Drug Discovery 400
A Step-by-Step Guide to Qualitative Data Coding 2nd Edition 400
Impact of Storage Orientation and Duration on Prefilled Syringe Performance: Break-Loose and Glide Forces, and Injection Time Across Multiple Time Points 360
Programming for Chemical Engineers Using C, C++, and MATLAB 300
Upland Kenya wild flowers and ferns: a flora of the flowers, ferns, grasses, and sedges of highland Kenya 300
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 物理 内科学 复合材料 催化作用 物理化学 光电子学 电极 细胞生物学 基因 无机化学
热门帖子
关注 科研通微信公众号,转发送积分 6670492
求助须知:如何正确求助?哪些是违规求助? 8418928
关于积分的说明 17996275
捐赠科研通 5880232
什么是DOI,文献DOI怎么找? 2977516
邀请新用户注册赠送积分活动 1953416
关于科研通互助平台的介绍 1882536