基于机器学习的氮掺杂石墨炔力学性能预测
Machine learning-based prediction of mechanical properties of N-dopedγ-graphdiyne
张存 1杨博林 2彭志龙 2陈少华3
作者信息
- 1. Department of Engineering Mechanics,Shijiazhuang Tiedao University,Shijiazhuang 050043,China;Hebei Key Laboratory of Intelligent Materials and Structures Mechanics,Shijiazhuang Tiedao University,Shijiazhuang 050043,China;Hebei Research Center of the Basic Discipline Engineering Mechanics,Shijiazhuang Tiedao University,Shijiazhuang 050043,China;Provincial Collaborative Innovation Center of Mechanics of Intelligent Materials in Hebei,Shijiazhuang Tiedao University,Shijiazhuang 050043,China
- 2. Institute of Advanced Structure Technology,Beijing Institute of Technology,Beijing 100081,China
- 3. Institute of Advanced Structure Technology,Beijing Institute of Technology,Beijing 100081,China;Beijing Key Laboratory of Lightweight Multi-functional Composite Materials and Structures,Beijing Institute of Technology,Beijing 100081,China
- 折叠
摘要
氮掺杂γ-石墨二炔(N-GDY)因其在能源、电子元器件和催化领域具有重要应用前景而备受关注.研究表明,N-GDY在不同的氮掺杂情况下会表现出迥异的物理化学性质.由于氮掺杂的多样性,N-GDY的理论及应用研究受到了极大的限制.鉴于此,本文采用鄂维南等人提出的DeepMD方法训练得到了具有第一性原理精度、适用于N-GDY的机器学习势.利用该机器学习势,系统研究了氮掺杂模式对N-GDY力学性能的影响.研究发现,氮原子掺杂会导致N-GDY的抗拉强度降低.在单个碳链上掺杂氮原子时,N-GDY的抗拉强度随着氮原子掺杂位点到苯环的距离变小而减弱.相邻碳链氮原子共掺杂能够使N-GDY表现出更强的各向异性力学特征.本文研究结果对N-GDY在能源存储和柔性设备等领域的潜在应用提供了理论支持,同时也表明了机器学习势在从大规模数据集中学习并预测碳纳米材料复杂力学性质方面的潜力,为纳米材料设计及工程应用具有重要指导作用.
Abstract
Nitrogen-doped γ-graphdiyne(N-GDY)has promising applications in energy,electronic devices,and cat-alysis,but its properties vary significantly with the distribu-tion of N-dopants and can be hardly investigated due to massive doping patterns.This work addressed the challenge through the machine-learning-based molecular dynamics si-mulations,and predicted the mechanical properties of N-GDY using a customized well-trained DeepMD-based machine learning potential(MLP).It is demonstrated that N-doping can undermine the ultimate tensile strength of N-GDY re-markably when the stress is applied along N-doped chains,particularly when the N-doping happens at the nearest carbon to the benzene ring.The synergetic effect of neighboring N-doped carbon chains on the anisotropic mechanical prop-erties of N-GDY has been further explored.This computa-tional effort not only clarifies the correlation between the tensile mechanical properties of N-GDY and N-doping pat-terns towards potential applications in energy storage and flexible devices,but also demonstrates the capacity of MLP to predict complicated mechanical properties of carbon nano-materials from massive datasets.
关键词
γ-graphdiyne/nitrogen-doping/mechanical proper-ties/DeepMD/molecular dynamicsKey words
γ-graphdiyne/nitrogen-doping/mechanical proper-ties/DeepMD/molecular dynamics引用本文复制引用
基金项目
国家自然科学基金(12032004)
国家自然科学基金(11872114)
国家自然科学基金(11502150)
河北省自然科学基金(A2016210060)
GHfund B(202202026154)
Key Project of Natural Science Foundation of Hebei Province(Basic Discipline Research)(A2023210064)
出版年
2024
NETL
NSTL
万方数据