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  1. NTU Theses and Dissertations Repository
  2. 工學院
  3. 應用力學研究所
請用此 Handle URI 來引用此文件: http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/22140
完整後設資料紀錄
DC 欄位值語言
dc.contributor.advisor張建成(Chien-Cheng Chang)
dc.contributor.authorYu-Xuan Wangen
dc.contributor.author王鈺軒zh_TW
dc.date.accessioned2021-06-08T04:04:39Z-
dc.date.copyright2018-08-01
dc.date.issued2018
dc.date.submitted2018-07-30
dc.identifier.citation1. Novoselov, K.S., A.K. Geim, S.V. Morozov, D. Jiang, Y. Zhang, S.V. Dubonos, I.V. Grigorieva, and A.A. Firsov, Electric Field Effect in Atomically Thin Carbon Films. Science, 2004. 306(5696): p. 666–669.
2. Wang, H., Y. Yang, Y. Liang, J.T. Robinson, Y. Li, A. Jackson, Y. Cui, and H. Dai, Graphene-wrapped sulfur particles as a rechargeable lithium-sulfur battery cathode material with high capacity and cycling stability. Nano Lett, 2011. 11(7): p. 2644-7.
3. Castro Neto, A.H., F. Guinea, N.M.R. Peres, K.S. Novoselov, and A.K. Geim, The electronic properties of graphene. Reviews of Modern Physics, 2009. 81(1): p. 109-162.
4. Li, X., Y. Zhu, W. Cai, M. Borysiak, B. Han, D. Chen, R.D. Piner, L. Colombo, and R.S. Ruoff, Transfer of Large-Area Graphene Films for High-Performance Transparent Conductive Electrodes. Nano Lett., 2009. 9(12): p. 4359-4363.
5. Schwierz, F., Graphene transistors. Nat Nanotechnol, 2010. 5(7): p. 487-96.
6. Lin, M.-Y., C.-H. Wang, S.-W. Chang, S.-C. Lee, and S.-Y. Lin, Passivated graphene transistors fabricated on a millimeter-sized single-crystal graphene film prepared with chemical vapor deposition. Journal of Physics D: Applied Physics, 2015. 48(29): p. 295106.
7. Mak, K.F., C. Lee, J. Hone, J. Shan, and T.F. Heinz, Atomically thin MoS2: a new direct-gap semiconductor. Phys Rev Lett, 2010. 105(13): p. 136805.
8. Wang, H., L. Yu, Y.H. Lee, Y. Shi, A. Hsu, M.L. Chin, L.J. Li, M. Dubey, J. Kong, and T. Palacios, Integrated circuits based on bilayer MoS2 transistors. Nano Lett, 2012. 12(9): p. 4674-80.
9. Yin, Z., H. Li, H. Li, L. Jiang, Y. Shi, Y. Sun, G. Lu, Q. Zhang, X. Chen, and H. Zhang, Single-Layer MoS2 Phototransistors. Nat. Nanotechnol, 2011. 6: p. 147–150.
10. Cai, Y., G. Zhang, and Y.W. Zhang, Polarity-reversed robust carrier mobility in monolayer MoS2 nanoribbons. J Am Chem Soc, 2014. 136(17): p. 6269-75.
11. Liu, X., G. Zhang, and Y.-W. Zhang, Thermal conduction across the one-dimensional interface between a MoS2 monolayer and metal electrode. Nano Research, 2016. 9(8): p. 2372-2383.
12. Song, Y., D. Li, W. Mi, X. Wang, and Y. Cheng, Electric Field Effects on Spin Splitting of Two-Dimensional van der Waals Arsenene/FeCl2 Heterostructures. The Journal of Physical Chemistry C, 2016. 120(10): p. 5613-5618.
13. Qiao, J., X. Kong, Z.X. Hu, F. Yang, and W. Ji, High-mobility transport anisotropy and linear dichroism in few-layer black phosphorus. Nat Commun, 2014. 5: p. 4475.
14. Li, L., Y. Yu, G.J. Ye, Q. Ge, X. Ou, H. Wu, D. Feng, X.H. Chen, and Y. Zhang, Black phosphorus field-effect transistors. Nat Nanotechnol, 2014. 9(5): p. 372-7.
15. Island, J.O., G.A. Steele, H.S.J.v.d. Zant, and A. Castellanos-Gomez, Environmental instability of few-layer black phosphorus. 2D Materials, 2015. 2(1): p. 011002.
16. Zhang, S., Z. Yan, Y. Li, Z. Chen, and H. Zeng, Atomically thin arsenene and antimonene: semimetal-semiconductor and indirect-direct band-gap transitions. Angew Chem Int Ed Engl, 2015. 54(10): p. 3112-5.
17. Ares, P., F. Aguilar-Galindo, D. Rodriguez-San-Miguel, D.A. Aldave, S. Diaz-Tendero, M. Alcami, F. Martin, J. Gomez-Herrero, and F. Zamora, Mechanical Isolation of Highly Stable Antimonene under Ambient Conditions. Adv Mater, 2016. 28(30): p. 6332-6.
18. Wu, X., Y. Shao, H. Liu, Z. Feng, Y.L. Wang, J.T. Sun, C. Liu, J.O. Wang, Z.L. Liu, S.Y. Zhu, Y.Q. Wang, S.X. Du, Y.G. Shi, K. Ibrahim, and H.J. Gao, Epitaxial Growth and Air-Stability of Monolayer Antimonene on PdTe2. Adv Mater, 2017. 29(11).
19. Geim, A.K. and I.V. Grigorieva, Van der Waals heterostructures. Nature, 2013. 499(7459): p. 419-25.
20. Wang, G., R. Pandey, and S.P. Karna, Atomically thin group v elemental films: theoretical investigations of antimonene allotropes. ACS Appl Mater Interfaces, 2015. 7(21): p. 11490-6.
21. Pizzi, G., M. Gibertini, E. Dib, N. Marzari, G. Iannaccone, and G. Fiori, Performance of arsenene and antimonene double-gate MOSFETs from first principles. Nat Commun, 2016. 7: p. 12585.
22. Xiao, J., M. Long, M. Li, X. Li, H. Xu, and K. Chan, Carrier mobility of MoS2 nanoribbons with edge chemical modification. Phys Chem Chem Phys, 2015. 17(10): p. 6865-73.
23. Kuc, A., N. Zibouche, and T. Heine, Influence of quantum confinement on the electronic structure of the transition metal sulfideTS2. Physical Review B, 2011. 83(24).
24. Rydberg, H., M. Dion, N. Jacobson, E. Schr¨oder, P. Hyldgaard, S.I. Simak, D.C. Langreth, and B.I. Lundqvist, Van der Waals Density Functional for Layered Structures. Phys. Rev. Lett., 2003. 91(12): p. 126402.
25. Ji, J., X. Song, J. Liu, Z. Yan, C. Huo, S. Zhang, M. Su, L. Liao, W. Wang, Z. Ni, Y. Hao, and H. Zeng, Two-dimensional antimonene single crystals grown by van der Waals epitaxy. Nat Commun, 2016. 7: p. 13352.
26. Koma, A., Van der Waals epitaxy—a new epitaxial growth method for a highly lattice-mismatched system. Thin Solid Films, 1992. 216(1): p. 72-76.
27. Chhowalla, M., H.S. Shin, G. Eda, L.J. Li, K.P. Loh, and H. Zhang, The chemistry of two-dimensional layered transition metal dichalcogenide nanosheets. Nat Chem, 2013. 5(4): p. 263-75.
28. Behler, J., Perspective: Machine learning potentials for atomistic simulations. The Journal of Chemical Physics, 2016. 145(17): p. 170901.
29. 張斐章 張麗秋, 類神經網路導論 : 原理與應用,第二版, ed. 鴻海圖書. 中華民國105年.
30. 羅華強, 類神經網路-MATLAB的應用,第三版, ed. 高立圖書. 中華民國100年.
31. Rumelhart, D.E., G.E. Hinton, and R.J. Williams, Learning representations by back-propagating errors. NATURE, 1986. 323(9).
32. Hinton, G.E. and R.R. Salakhutdinov, Reducing the Dimensionality of Data with Neural Networks. Science, 2006. 313(1129198): p. 504-507.
33. 林大貴, TensorFlow+Keras:深度學習人工智慧實務應用,初版, ed. 博碩文化. 中華民國106年.
34. Marx, D. and J.u. Hutter, Ab initio molecular dynamics: Theory and Implementation. Modern Methods and Algorithms of Quantum Chemistry, J. Grotendorst (Ed.), John von Neumann Institute for Computing, J¨ ulich, NIC Series,, 2000. 1(ISBN 3-00-005618-1): p. 301-449.
35. Born, M. and J.R. Oppenheimer, On the Quantum Theory of Molecules. Ann. Physik, 1927. 389(20): p. 457-484.
36. K¨uhne, T.D., Ab-Initio Molecular Dynamics. Institute of Physical Chemistry and Center for Computational Sciences, Johannes Gutenberg University Mainz, Staudinger Weg 7, D-55128 Mainz, Germany, 2013.
37. Hohenberg, P. and W. Kohn, Inhomogeneous Electron Gas. Physical Review, 1964. 136(3B): p. B864-B871.
38. Kohn, W. and L.J. Sham, Self-Consistent Equations Including Exchange and Correlation Effects. Physical Review, 1965. 140(4A): p. A1133-A1138.
39. Kohn, W. and L.J. Sham, Quantum Density Oscillations in an Inhomogeneous Electron Gas. Physical Review, 1965. 137(6A): p. A1697-A1705.
40. Perdew, J.P., K. Burke, and M. Ernzerhof, Generalized Gradient Approximation Made Simple. PHYSICAL REVIEW LETTERS, 1996. 77(18): p. 3865(4).
41. Payne, M.C., M.P. Teter, D.C. Allan, T.A. Arias, and J.D. Joannopoulos, Iterative minimization techniques forab initiototal-energy calculations: molecular dynamics and conjugate gradients. Reviews of Modern Physics, 1992. 64(4): p. 1045-1097.
42. Blöchl, P.E., Projector augmented-wave method. Physical Review B, 1994. 50(24): p. 17953-17979.
43. Feynman, R.P., Forces in Molecules. Physical Review, 1939. 56(4): p. 340-343.
44. Kresse, G., M. Marsman, and J.u. Furthm¨uller, VASP the GUIDE. Computational Materials Physics, Faculty of Physics, Universit¨at Wien, Vienna, April 20, 2016.
45. Monkhorst, H.J. and J.D. Pack, Special points for Brillouin-zone integrations. Physical Review B, 1976. 13(12): p. 5188-5192.
46. Pack, J.D. and H.J. Monkhorst, 'Special points for Brillouin-zone integrations'—a reply. Physical Review B, 1977. 16(4): p. 1748-1749.
47. Roux, S.L. and P.V. Petkov. Available from: http://isaacs.sourceforge.net/phys/pbc.html.
48. Bloeh, F., Uber die Quantenmechanik der Elek~ronen in Kristallgittern. Zeitschrift fur Physik A Hadrons and Nuclei, 1929. 52: p. 555.
49. GiBBS., J.W., On the Equilibrium of Heterogeneous Substances. American Journal of Science, 1878(96): p. 441-458.
50. Lemons, D.S. and A. Gythiel, Paul Langevin’s 1908 paper “On the Theory of Brownian Motion” [“Sur la théorie du mouvement brownien,” C. R. Acad. Sci. (Paris) 146, 530–533 (1908)]. American Journal of Physics, 1997. 65(11): p. 1079-1081.
51. MODULES TUTORIALS_Tutorials. 2017.
52. Day, N. Crystallography Open Database. Available from: http://www.crystallography.net/cod/index.php.
53. Khorshidi, A. and A.A. Peterson, Amp : A modular approach to machine learning in atomistic simulations. Computer Physics Communications, 2016. 207: p. 310-324.
54. Andrew A. Peterson, A.K., Amp Documentation. Sep 20, 2017.
55. Hjorth Larsen, A., J. Jorgen Mortensen, J. Blomqvist, I.E. Castelli, R. Christensen, M. Dulak, J. Friis, M.N. Groves, B. Hammer, C. Hargus, E.D. Hermes, P.C. Jennings, P. Bjerre Jensen, J. Kermode, J.R. Kitchin, E. Leonhard Kolsbjerg, J. Kubal, K. Kaasbjerg, S. Lysgaard, J. Bergmann Maronsson, T. Maxson, T. Olsen, L. Pastewka, A. Peterson, C. Rostgaard, J. Schiotz, O. Schutt, M. Strange, K.S. Thygesen, T. Vegge, L. Vilhelmsen, M. Walter, Z. Zeng, and K.W. Jacobsen, The atomic simulation environment-a Python library for working with atoms. J Phys Condens Matter, 2017. 29(27): p. 273002.
56. Perdew, J.P., K. Burke, and M. Ernzerhof, Generalized Gradient Approximation Made Simple. PHYSICAL REVIEW LETTERS, 1996. 17(18): p. 3865-3968.
57. Kresse, G. and D. Joubert, From ultrasoft pseudopotentials to the projector augmented-wave method. The American Physical Society, 1999. 59(3): p. 1758~1775.
58. Grimme, S., Semiempirical GGA-type density functional constructed with a long-range dispersion correction. J Comput Chem, 2006. 27(15): p. 1787-99.
59. Chuang, F.-C., C.-H. Hsu, C.-Y. Chen, Z.-Q. Huang, V. Ozolins, H. Lin, and A. Bansil, Tunable topological electronic structures in Sb(111) bilayers: A first-principles study. APPLIED PHYSICS LETTERS, 2013. 102(2): p. 022424/1-4.
60. Gonze, X., J.P. Michenaud, and J.P. Vigneron, First-principles study of As, Sb, and Bi electronic properties. Physical Review B, 1990. 41(17): p. 11827-11836.
61. Feng, L.-p., J. Su, S. Chen, and Z.-t. Liu, First-principles investigations on vacancy formation and electronic structures of monolayer MoS2. Materials Chemistry and Physics, 2014. 148(1-2): p. 5-9.
62. Kumar, A. and P.K. Ahluwalia, Semiconductor to metal transition in bilayer transition metals dichalcogenidesMX2(M= Mo, W;X= S, Se, Te). Modelling and Simulation in Materials Science and Engineering, 2013. 21(6): p. 065015.
63. Peng, Q. and S. De, Outstanding mechanical properties of monolayer MoS2 and its application in elastic energy storage. Phys Chem Chem Phys, 2013. 15(44): p. 19427-37.
64. Ratnasamy, P., L. Rodrique, and A.J. Leonard, Structural and Textural Studies in Molybdenum Sulfide Systems. The Journai of Physical Chemistry, 1973. 77(18).
65. Behler, J., Atom-centered symmetry functions for constructing high-dimensional neural network potentials. J Chem Phys, 2011. 134(7): p. 074106.
66. Fletcher and Roger, Practical methods of optimization (2nd ed.). 1987, New York: John Wiley & Sons.
67. Larsen, A.H., J.J. Mortensen, J. Blomqvist, I.E. Castelli, R. Christensen, M. Dułak, J. Friis, M.N. Groves, B. Hammer, C. Hargus, E.D. Hermes, P.C. Jennings, P.B. Jensen, J. Kermode, J.R. Kitchin, E.L. Kolsbjerg, J. Kubal, K. Kaasbjerg, S. Lysgaard, J.B. Maronsson, T. Maxson, T. Olsen, L. Pastewka, A. Peterson, C. Rostgaard, J. Schiøtz, O. Schütt, M. Strange, K.S. Thygesen, T. Vegge, L. Vilhelmsen, M. Walter, Z. Zeng, and K.W. Jacobsen, The Atomic Simulation Environment—A Python library for working with atoms. J. Phys.: Condens. Matter, 2017. 29(273002).
68. Stukowski, A., Visualization and Analysis Strategies for Atomistic Simulations - Manual.
69. Chen, L.D., The melting temperature calculation of silicon bulk and silicon quantum dots by ab-initio molecular dynamics simulation. Master's thesis, National Tsing Hua University, 7 2007.
70. Chi, S.F., First Principles Molecular Dynamics Analysis on the Molten Electrolytes pd Thermal Batteries. Master's thesis, National Tsing Hua University, 7 2014.
71. Huang, Y.R., Modifying CNT to Mimic Dinuclear Organometallic Catalyst. Master's thesis, National Taiwan Normal University, 7 2016.
72. Yu, D.M., A Neural-networks-based Eye-Tracking System. Master's thesis, National Central University, 6 2017.
73. Jhan, S.C., First Principle Investigation on the Photo Thermoelectric Properties of MAPbI3-xClx Perovskites. Master's thesis, National Tsing Hua University, 7 2017.
74. Tsai, Y.T., An investigation of mechanical and thermal properties of two-dimensional boron carbon nitride by molecular dynamics. Master's thesis, National Taiwan University, 1 2017.
75. Ke, Y.C., An Investigation of Thermal and Mechanical Properties of Two Dimensional Graphene-Boron Nitride Heterostructures by Atomistic Simulations. Master's thesis, National Taiwan University, 7 2017.
dc.identifier.urihttp://tdr.lib.ntu.edu.tw/jspui/handle/123456789/22140-
dc.description.abstract銻烯是一種類似於石墨烯的翹曲六方晶格排列的結構,伴隨著原子上下交錯排列,其優異的半導體電學性質甚至優於其他二維材料,且在大氣中也不易與空氣產生氧化降解。但是其製備過程與其他二維材料相比非常不易,科學家思考利用異質結構方式獲得大面積高品質的銻烯,表面化學穩定的過渡金屬二硫屬化合物便成為異質結構作為基板的最佳選擇。本研究嘗試以分子模擬研究銻烯在二硫化鉬基板上的結構性質,探討二硫化鉬是否適合作為銻烯生長的基板。
對於分子模擬而言,第一原理計算能夠提供最精確的原子間作用力。然而,第一原理計算十分耗費計算與時間資源,而凡得瓦異質結構因為要考慮misfit strain乃至扭曲角,使得模擬系統大小往往遠超其所能處理的範圍。古典分子動力模擬能考慮更大的系統,然而對古典分子模擬而言,分子勢場是不可或缺的。對於複雜的系統來說,由於複雜化學組成以及原子間交互作用,使得建立分子勢場十分具有挑戰性。本研究採用機器學習-類神經網路方法訓練異質結構分子勢能,稱之為神經網路勢能,既有著經驗勢的速度且精準度也直逼第一原理的計算。異質結構分子勢場訓練過程系將上萬筆銻、二硫化鉬,以及異質結構及相對應的以第一原理計算之系統能量輸入訓練集並進行訓練。經過訓練的神經網路模型其驗證及預測的結果均與相對應的第一原理計算結果吻合,證明此一分子勢能具有高度精確性。
我們以訓練完成的神經網路勢能場進行分子模擬測試兩個約一千顆原子異質結構在室溫300K下的系統穩定性,我們的模擬結果顯示兩個系統溫度和能量皆穩定,更重要的是,與第一原理分子動力學相比,利用神經網路勢能進行分子動力學計算有著更高的計算效率。接著,我們利用神經網路勢能對異質結構進行結構優化,採取的優化演算法為BFGS和共軛梯度法,我們藉由比較兩種優化算法最終結構演示了神經網路勢能的侷限。最後,我們發現銻烯因二硫化鉬基板引入的壓縮應變,導致其六角環結構產生扭曲現象。本論文顯示了神經網路模型能夠快速精確的估算化學複雜系統,如凡得瓦異質結構之系統能量,從而能夠進行更大尺度的分子模擬以探索其結構性質。
zh_TW
dc.description.abstractAntimonene is a structure similar to graphene with twisted hexagonal rings and staggered antimony atoms with superior electronic properties over other two-dimensional materials as well as stability under ambient condition. However, fabrication of high-quality antimonene is difficult relative to other two-dimensional materials. One promising solution is to make use of layered transition-metal-dichalcogenide (TMD) material as growing template, and grow antimonene nanosheets atop, forming van der Waals heterostuctures. In this study, we explored the possibilities of utilizing molybdenum disulfide (MoS2) as the growing template of the vdW heterostructure by performing molecular simulations to examine the structural properties of antimonene grown on MoS2.
The most accurate measure of molecular simulation of antimonene/MoS2 heterostructures is ab initio molecular simulations. However, ab initio calculations are extremely computationally expensive, making them literally unusable for vdW heterostructures, which misfit strains or even twist angles must be taken into account. Classical molecular simulations can overcome system size issues. However, interatomic potential is the key component of classical molecular simulations, and it is extremely difficult to parameterize an interatomic potential for vdW heterostructures due to complex compositions and interactions. In this study, we harnessed the power of machine learning and constructed an artificial neural network (ANN) model to evaluate system energies with high fidelity to respective ab initio calculation for given structures. We anticipate that ANN model can have both of the computation efficiency of classical interatomic potential and the accuracy of the ab initio calculations. The ANN model was obtained by extensive training processes. In the training processes tens of thousands of structures of bulk antimonene, MoS2, and Sb/MoS2 heterostructures along with their energies from ab initio calculations were fed into the training sets. The trained ANN model can successfully evaluate energy of structures from both the training sets and validation sets with excellent agreements with those from ab initio calculations, suggesting that this trained ANN potential is robust and can be utilized for molecular simulations. We then performed classical molecular simulations of two different sb/MoS2 heterostructures with system size around one thousand atoms - a system size almost beyond the reach of ab initio calculations - using the trained ANN potential. Both systems were stable during the classical molecular simulations, and we also demonstrate that molecular simulations using the ANN potential yields much higher computation efficiency relative to ab initio molecular simulations. Next, we tested the ANN potential by performing structural optimization of Sb/MoS2 heterostructure by using both BFGS conjugate gradient minimizer, and we reveal the limitations of ANN potential model. Finally, we examined the optimized monolayer Sb/MoS2 heterostructure, and we observed distorted hexagonal rings in antimonene due to compressive misfit strains imposed on monolayer antimonene. The present study demonstrated that ANN model is a powerful tool in evaluating energies/forces of chemically complex systems such as vdW heterostructures with high accuracy, thereby allowing large scale molecular simulations for exploration of structural properties of vdW heterostructures.
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dc.description.tableofcontents致謝 i
摘要 ii
Abstract iii
目錄 v
圖目錄 viii
表目錄 xii
第一章 緒論 1
1.1 前言 1
1.2 銻烯、二硫化鉬及異質結構簡介 3
1.2.1 原子結構及特性 3
1.2.2 製備方式 6
1.3 研究動機 9
1.4 文章架構 11
第二章 理論與計算原理 12
2.1 類神經網路 12
2.1.1 類神經網路簡介 12
2.1.2 類神經元模型 13
2.1.3 類神經網路架構與學習方式 15
2.1.4 倒傳遞類神經網路 17
2.1.5 深度神經網路 23
2.2 第一原理分子動力學 24
2.2.1 前言 24
2.2.2 波恩-歐本海默近似(Born-Oppenheimer approximation) 25
2.2.3 密度泛函理論(Density Functional Theory, DFT) 26
A. Hohenberg-Kohn Theorem 27
B. Kohn-Sham Equation 28
2.2.3 交換相關能(Electron Exchange-Correlation Energy) 29
A.局部密度近似法(LDA) 29
B.廣義梯度近似法(GGA) 30
2.2.4 贋勢(Pseudopotential Approximation) 31
2.2.5 平面波投影法(Project Augmented Waves, PAW) 32
2.2.6 赫爾曼-費恩曼定理(Hellmann-Feynman theorem) 34
2.2.7 VASP(Vienna Ab-initio Simmulation Package) 34
A.週期性邊界(Periodic Boundary Condition, PBC) 35
B.布洛赫定理(Bloch theorem) 35
2.2.8 VASP Ab-initio Molecular Dynamics(AIMD) 37
2.2.9 統計模型 38
A. Langevin Thermostat 41
第三章 模擬流程與模型建構 43
3.1 模擬流程 43
3.1.1 VASP設定 45
3.1.2 二維Sb-MoS2異質結構建模 46
A. Sb Bulk / Antimonene 46
B. MoS2 Bulk / Single layer MoS2 47
C. Sb-MoS2異質結構材料(Heterostructure) 48
3.1.3 分子勢能場(Atomic Potential)訓練 50
A.分子機器學習套件(AMP) 50
B.訓練集(Training sets) 54
C.訓練過程 57
3.1.4 勢能後計算 58
第四章 結果與討論 59
4.1 簡介 59
4.2 銻(Sb)勢能訓練 60
4.2.1 勢能驗證(Sb Potential Validation) 60
4.2.2 勢能預測(Sb Potential Prediction) 63
4.3 二硫化鉬(MoS2)勢能訓練 67
4.3.1 勢能驗證(MoS2 Potential Validation) 67
4.3.2 勢能預測(MoS2 Potential Prediction) 70
4.4 Sb-MoS2 異質結構勢能訓練 74
4.4.1 勢能驗證(Sb-MoS2 heterostructure Potential Validation) 75
4.4.2 勢能預測(Sb-MoS2 heterostructure Potential Prediction) 80
4.5 異質結構勢能-分子模擬 85
4.5.1 分子動力學模擬 86
A.銻塊材-二硫化鉬塊材(Sb bulk-MoS2 bulk) 86
B.銻烯-二維二硫化鉬(Antimonene-MoS2 single layer) 89
C.徑向分佈函數(Radial Distribution Function, RDF) 91
4.5.2 結構優化模擬 93
A. BFGS優化 94
B. MDmin優化 95
C.優化結構觀察 98
第五章 結論與未來展望 100
5.1 結論 100
5.2 未來展望 101
參考文獻 102
附錄 110
dc.language.isozh-TW
dc.title以機器學習方法訓練二維凡德瓦異質結構之分子勢場zh_TW
dc.titleModeling van der Waals’ force of 2D Materials Using
Interatomic Potentials from Artificial Neural Networks
en
dc.typeThesis
dc.date.schoolyear106-2
dc.description.degree碩士
dc.contributor.coadvisor包淳偉(Chun-Wei Pao)
dc.contributor.oralexamcommittee朱錦州(Chin-Chou Chu),趙聖德(Sheng-Der Chao),楊瑞珍
dc.subject.keyword二維材料,銻,二硫化鉬,異質結構,第一原理,密度泛函理論,分子動力學,機器學習,類神經網路,分子勢場,zh_TW
dc.subject.keywordTwo Dimensional materials,Antimonene,MoS2,Heterostructure,Ab initio,Density function theory,Molecular dynamics,Machine Learning,Artificial Neural Networks,Interatomic Potential,en
dc.relation.page118
dc.identifier.doi10.6342/NTU201801652
dc.rights.note未授權
dc.date.accepted2018-07-31
dc.contributor.author-college工學院zh_TW
dc.contributor.author-dept應用力學研究所zh_TW
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