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研究生： 王武傑
Wang, Wu-Chieh
論文名稱： 複合材料含奈米碳管與石墨烯之分子動力學模擬
Molecular dynamics simulation of composite materials containing carbon nanotubes and graphene
指導教授： 王雲哲
Wang, Yun-Che
學位類別： 碩士
Master
系所名稱： 工學院 - 土木工程學系
Department of Civil Engineering
論文出版年： 2019
畢業學年度： 107
語文別： 英文
論文頁數： 146
中文關鍵詞： 分子動力學 、第一原理計算 、機器學習 、石墨烯 、奈米碳管 、複合材料 、高分子材料
外文關鍵詞： Molecular dynamics simulation, First principle calculation, Machine learning, Graphene, Carbon Nanotube, Composite Materials, Polymer
相關次數： 點閱：144  下載：0 
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    • 奈米碳管(CNT)和單層石墨烯因其具有優異的力學、導熱和電傳導性質而廣為人知， 將CNT和/或石墨烯做為添加物的複合材料可以擁有比其基材更良好的行為表現，如有效地提升彈性模數、抗拉與抗壓的強度、韌性、阻尼、導熱和導電性質等。本論文藉 由第一原理計算探討CNT的電性，電子在CNT中因彈道傳輸模式，使其具有優異的導電性質。此外，由於彈道聲子傳輸，CNT和石墨烯也同時具有良好的熱導率。本研究將 混合型的CNT-石墨烯(HCG)結構嵌入基材中，例如水化矽酸鈣(C-S-H) 或高分子材料尼龍-6，並將此複合材料以分子動力學(MD)進行模擬分析，發現整體的力學性質和熱性 質，皆因HCG而有效地提升。根據計算結果，嵌入HCG 的C-S-H (CSHnHCG)在各個單軸加載下，在應變0.05時，等效楊氏模數和強度皆有2到3倍的增加。其中當加載方向平 行於CNT走向，因碳管受到圍束效果，在受壓時強度和楊氏模數可達原先約5倍，其破壞模式主要是HCG結構的挫屈行為，而受拉時應力和楊氏模數可達原先的約4倍，最終 則因HCG的解體而破壞。此外，嵌入在尼龍-6中的HCG，若其CNT直徑過小，導致周圍尼龍-6分子具有足夠的力量吸引彼此，進而嚴重擠壓CNT，破壞其管狀結構，使整體的 力學行為及熱傳導能力無法同CSHnHCG一樣有效地提升，此現象可類比蟒蛇獵食理論解釋之。在熱性質計算中，本文透過Green-Kubo平衡法求得在環境溫度300K時的熱傳 導係數，CSHnHCG從24.87提升至232.48 W·m-1·K-1，而NlonnHCG從16.04提升至33.67 W·m-1·K-1。本文亦探討以機器學習建立神經網路勢能(NNP)之方法，以量子分子動力學 計算的結果做為訓練數據庫，訓練神經網路的權重參數，並應用此參數於水分子之計算模擬。

    Carbon nanotubes (CNT’s) and single layers of graphene are widely known for their superior mechanical, thermal and electrical properties. Composite materials containing the CNT’s and/or graphene may exhibit enhanced effective properties, such as enhanced elastic modulus. In this work, the electric properties of CNT’s is studied via the first-principle calculations. Due to the ballistic mode of electron transport, exceptionally good electric conductivity is found in CNT’s. Similarly, thermal conductivity in CNT’s and graphene can be largely increased due to the ballistic phonon transport. When hybrid CNT-graphene (HCG) structures are embedded in a matrix, such as calcium-silicate-hydrate (CSH) or polymer nylon-6, the overall mechanical and thermal properties are studied via conventional molecular dynamics (MD) simulation. It is found that structured HCG inclusion can increase the effective Young’s modulus and strength by a factor of 2 to 3. When loading direction is parallel to the CNT’s, compressive strength and Young’s modulus were increased by a factor of 5. Its tensile strength and Young’s modulus were increased by a factor of 4. Under compression, the failure mode of the composite is dominated by buckling of the HCG structures. Under large tension, the composite fails due to the fracture of the HCG inclusion. When the radius of CNT is small, nylon chain molecules may behave like a python snake to break CNT by severe wrapping. It is found that, from the Green-Kubo method, the thermal conductivity coefficient of CSGnHCG increases from 24.87 to 232.48 W m-1 K-1, and that of NylonnHCG increases from 16.04 to 33.67 W m-1 K-1. Neural network interatomic potentials were studied via machine learning techniques to convert atomic interactions from quantum-molecular-dynamics simulations into neural network weights and biases for classical MD simulations.

    CHINESE ABSTRACT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . i ABSTRACT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ii ACKNOWLEDGMENTS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii LIST OF TABLES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii LIST OF FIGURES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix NOMENCLATURE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xvii 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.1 Goals and motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Literature review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2 1.2.1 Sensational materials - carbon nanotubes and graphene . . . . . . . . . 2 1.2.2 C-S-H research by MD . . . . . . . . . . . . . . . . . . . . . . . . . . 6 1.3 Outline of this thesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 2 Theoretical Backgrounds . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 2.1 Born-Oppenheimer approximation . . . . . . . . . . . . . . . . . . . . . . . . 9 2.2 The first principle theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 2.3 Density functional theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 2.3.1 Honenbrerg-Kohn and Kohn-Sham ansatz . . . . . . . . . . . . . . . 11 2.3.2 The exchange-correlation functional . . . . . . . . . . . . . . . . . . . 13 2.4 Quantum transport for electronic conductivity . . . . . . . . . . . . . . . . . . 13 2.4.1 Electron transfer mechanism in conductor . . . . . . . . . . . . . . . . 14 2.4.2 Non-equilibrium Green’s function . . . . . . . . . . . . . . . . . . . . 15 2.4.3 Total current . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 2.4.4 The density of state . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 2.5 Molecular dynamics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 2.6 Mechanical loss tangent . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 2.7 Thermal conductivity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 3 Computational information . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 3.1 Setup of the electronic transport . . . . . . . . . . . . . . . . . . . . . . . . . 28 3.2 Atomistic models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 3.2.1 Hybrid CNTs/Graphene . . . . . . . . . . . . . . . . . . . . . . . . . 28 3.2.2 C-S-H model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 3.2.3 Nylon model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 3.3 Neural network potential . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37 4 Results and discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .40 4.1 Electronic transport in single-walled carbon nanotubes . . . . . . . . . . . . . 40 4.1.1 System establishment and clarification . . . . . . . . . . . . . . . . . . 40 4.1.2 Ballistic transport properties in single-walled carbon nanotubes . . . . 43 4.2 Hybrid composite material CNTs/Graphene . . . . . . . . . . . . . . . . . . . 47 4.2.1 Model building and optimization . . . . . . . . . . . . . . . . . . . . . 47 4.2.2 Multiscale mechanical simulation of hybrid CNTs/Graphene . . . . . . 51 4.2.2.1 Multiscale mechanical performance of HCG . . . . . . . . . 51 4.2.2.2 Effects of loading rates . . . . . . . . . . . . . . . . . . . . 56 4.3 Mechanical properties of hybrid CNT/Graphene structure on C-S-H . . . . . . 65 4.3.1 C-S-H and hybrid CNTs/Graphene composite model . . . . . . . . . . 65 4.3.2 Uniaxial loading tests on the CSH and composite model . . . . . . . . 67 4.3.3 Oscillatory loading . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74 4.4 Mechanical properties of hybrid CNT/Graphene structure on nylon . . . . . . . 76 4.4.1 Mechanical properties of nylon . . . . . . . . . . . . . . . . . . . . . 76 4.4.2 Non-bonded interface interactions . . . . . . . . . . . . . . . . . . . . 78 4.4.3 Nylon and hybrid CNTs/Graphene composite model . . . . . . . . . . 81 4.4.4 Uniaxial loading tests on the nylon and composite model . . . . . . . . 84 4.4.5 Oscillatory loading . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91 4.5 Thermal properties of CNT/Graphene structure on nylon and CSH . . . . . . . 93 4.6 Neural network potentials application . . . . . . . . . . . . . . . . . . . . . . 94 4.6.1 Training process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94 4.6.2 Neural network potential training . . . . . . . . . . . . . . . . . . . . 96 5 Conclusion and future work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99 5.1 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99 5.2 Future work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102 LIST OF REFERENCES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103 APPENDICES Appendix A: Python code 1: From LAMMPS to SIESTA FDF format . . . . . . . 107 Appendix B: Python code 2: From SIESTA to neural network potential . . . . . . 110 Appendix C: Presentation slides . . . . . . . . . . . . . . . . . . . . . . . . . . . 114 VITA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 146 Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .147

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