請用此 Handle URI 來引用此文件：
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/96261完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 趙聖德 | zh_TW |
| dc.contributor.advisor | Sheng-Der Chao | en |
| dc.contributor.author | 林義達 | zh_TW |
| dc.contributor.author | Yi-Ta Lin | en |
| dc.date.accessioned | 2024-11-28T16:27:32Z | - |
| dc.date.available | 2024-11-29 | - |
| dc.date.copyright | 2024-11-28 | - |
| dc.date.issued | 2024 | - |
| dc.date.submitted | 2024-11-18 | - |
| dc.identifier.citation | [1] Chao, S.W., Li ,A. H. T., ＆ Chao, S. D.(2009). Molecular dynamics simulation of fluid methane properties using ab initio intermolecular interaction potential. Journal of computational chemistry, 30(12), 1839-1849.
[2] Huang, W. J., ＆ Chao, S. D.(2021). Quantum chemistry calculated intermolecular interaction and molecular dynamics simulation of chloromethane、chloroform molecules. National Taiwan University, Institute of Applied Mechanics. [3] Chen, Q. S., ＆ Chao, S. D.(2019).Quantum chemistry calculated intermolecular interaction and molecular dynamics simulation of dichloromethane. National Taiwan University, Institute of Applied Mechanics. [4] Ching-Hsiang Yu., & Chao, S. D.(2022).Using Ab initio Principle Molecular Dynamics Simulations to explore the Apollo Configurations of Chloroform and Bromoform Molecules. [5] Li, A. H. T., Huang, S. C., ＆ Chao, S. D.(2010).Molecular dynamics simulation of liquid carbon tetrachloride using ab initio force field. The Journal of chemical physics, 132(2), 024506. [6] Li, A. H. T., Huang, S. C., ＆ Chao, S. D.(2010).Molecular dynamics simulation of liquid carbon tetrachloride using ab initio force field. The Journal of chemical physics, 132(2), 024506. [7] Max Planck, "On the Law of Energy Distribution in the Normal Spectrum," Annals of Physics, vol. 309, no. 3, pp. 553-563, (1901). [8] Albert Einstein, "On a heuristic point of view concerning the production and transformation of light," Annals of Physics, vol. 17, no. 6, pp. 132-148, (1905). [9] Louis de Broglie, " Quantum Theory Research," Annales de Physique, vol. 3, no. 22, pp. 22-128, (1925). [10] Werner Heisenberg, " On the quantum theoretical reinterpretation of kinematic and mechanical relationships," Journal of Physics, vol. 33, no. 12, pp. 879-893, (1925). [11] Erwin Schrödinger, " Quantization as an eigenvalue problem," Annals of Physics, vol. 79, no. 4, pp. 361-376, (1926). [12] Paul A. M. Dirac, "The Quantum Theory of the Electron," Proceedings of the Royal Society A, vol. 117, no. 778, pp. 610-624, (1928). [13] Stone, A. J.(1996). The theory of Intermolecular Force. International Series of Monographs on Chemistry, Vol.32. [14] Dunning, T. H.(2000). A road map for the calculation of molecular binding energies. The Journal of Physical Chemistry A, 104(40), 9062-9080. [15] Parker, T. M., Parrish, R. M., Burns, L. A., Ryno, A.G., ＆ Sherrill, C. D.(2014). Levels of symmetry adapted perturbation theory (SAPT). Efficiency and performance for interaction energies. The Journal of Chemical Physics, 140(9), 094106. [16] Stone, A.(2013). The theory of intermolecular forces. OUP Oxford. [17] Grimme, S.,Ehrlich, S., Antony, J., ＆ Krieg, H.(2010). A consistent and accurate ab initio parametrization od density functional dispersion correction (DFT-D) for the 94 elements H-Pu. The Journal of Chemical Physics, 132(15), 154104. [18] Marchetti, O., ＆ Werner, H. J.(2009).Accurate calculations of intermolecular interaction energies using explicitly correlated coupled cluster wave functions and a dispersion-weighted MP2 method. The Journal of Physical Chemistry A, 113(43), 11580-11585. [19] Raghavachari, K., Pople, J. A., Trucks, G. W., ＆ Head-Gordon, M. (1989). A fifth-order perturbation comparison of electron correlation theories. Chemical Physics Letters, 157(6), 479-483. [20] Jeziorski, B., Moszynski, R., ＆ Szalewicz, K.(1994).Perturbation theory approach to intermolecular potential energy surfaces of van der Waals complexes. Chemical Review, 94(7), 1887-1930. [21] Marques, M. A., ＆ Gross, E. K.(2004). Time-dependent density functional theory. Annual Review of Physics Chemistry, 55, 427-455. [22] Hastie, T., Tibshirani, R., & Friedman, J. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer. [23] Sutton, R. S., & Barto, A. G. (2018). Reinforcement Learning: An Introduction. MIT Press. [24] Bishop, C. M. (2006). Pattern Recognition and Machine Learning. Springer. [25] Aidan P. Thompson, H. Metin Aktulga, Richard Berger, Dan S. Bolintineanu, W. Michael Brown, Paul S. Crozier, Pieter J. in 't Veld, Axel Kohlmeyer, LAMMPS - a flexible simulation tool for particle-based materials modeling at the atomic, meso, and continuum scales, Computer Physics Communications, Vol 271, 108171, 2022. [26] H. Wang, L. Zhang, J. Han, and W. E, DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics, Computer Physics Communications, Vol 228, Pages 178-184, 2018. [27] Freund, Y. and R.E. Schapire, Large margin classification using the perceptron algorithm. Machine learning, 1999. 37(3): p. 277-296. [28] Hinton, G.E., Distributed representations. 1984. [29] Rumelhart, D. and J. McClelland, Group, PR Parallel Distributed Processing: Explorations in the Microstructures of Cognition. Volume 1: Foundations. 1986, The MIT Press: Cambridge, MA, USA. [30] Sazli, M.H., A brief review of feed-forward neural networks. Communications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering, 2006. 50(01). [31] Jose, K.J., N. Artrith, and J. Behler, Construction of high-dimensional neural network potentials using environment-dependent atom pairs. The Journal of chemical physics, 2012. 136(19): p. 194111. [32] Han, J. and C. Moraga. The influence of the sigmoid function parameters on the speed of backpropagation learning. in International workshop on artificial neural networks. 1995. Springer. [33] Thomas D. Kühne, Augustin Bussy, Fabian Belleflamme, Gloria Tabacchi, Andreas Glöß, Michael Lass, Iain Bethun; CP2K: An electronic structure and molecular dynamics software package - Quickstep: Efficient and accurate electronic structure calculations. J. Chem. Phys. 21 May 2020; 152 (19): 194103. [34] Donchev, A.G., Taube, A.G., Decolvenaere, E. et al. Quantum chemical benchmark databases of gold-standard dimer interaction energies. Sci Data 8, 55 (2021). [35] Deep potential molecular dynamics: a scalable model with the accuracy of quantum mechanics L Zhang, J Han, H Wang, R Car, E Weinan - Physical review letters, 2018 [36] Wenshuo Liang, Guimin Lu, Jianguo Yu, Theoretical prediction on the local structure and transport properties of molten alkali chlorides by deep potentials,Journal of Materials Science & Technology,Volume 75,2021,Pages 78-85,ISSN 1005-0302, [37] Jörg Behler, Four Generations of High-Dimensional Neural Network Potentials. Chemical Reviews 2021 121 (16), 10037-10072 [38] Zaheer, M. et al. Deep sets. In Advances in Neural Information Processing Systems 30, 3391–3401 (2017) [39] Linfeng Zhang, Han Wang, Weinan E; Reinforced dynamics for enhanced sampling in large atomic and molecular systems. J. Chem. Phys. 28 March 2018; 148 (12): 124113. [40] Szabo, A., & Ostlund, N. S. (1996). Modern Quantum Chemistry: Introduction to Advanced Electronic Structure Theory. Dover Publications. [41] Troullier, N., & Martins, J. L. (1991). "Efficient Pseudopotentials for Plane-Wave Calculations." Physical Review B, 43(3), 1993-2006. [42] Perdew, J. P., Burke, K., & Ernzerhof, M. (1996). "Generalized Gradient Approximation Made Simple." Physical Review Letters, 77(18), 3865-3868. [43] Perdew, J. P., Ruzsinszky, A., & Csonka, G. I. (2005). "Workhorse functionals for condensed matter: PBE and beyond." Journal of Chemical Physics, 123(6), 062201.. [44] Goedecker, S., Teter, M., & Hutter, J. (1996). "Separable Dual Space Gaussian Pseudopotentials." Physical Review B, 54(3), 1703-1710. [45] Korn, R., & Kresse, G. (2008). "The GTH Pseudopotentials in VASP: A Comparison of the Atomic and Solid-State Calculations." Computational Materials Science, 44(3), 1145-1155. [46] Madden, P. A., & Goniakowski, J. (2004). "Theoretical and Computational Chemistry: An Overview." Theoretical Chemistry Accounts, 112(2), 175-182. [47] Vydrov, O. A., & van Voorhis, T. (2009). "Nonlocal van der Waals density functional: The simple manual." Journal of Chemical Physics, 130(18), 184103. [48] Grimme, S. (2011). "Improved DFT-D3 dispersion correction: Consideration of the damping function and comparison with the DFT-D2 method." Journal of Computational Chemistry, 32(5), 1456-1465. [49] Grimme, S., Ehrlich, S., & Goerigk, L. (2011). "An improved variant of the D3 dispersion correction for density functional theory." Journal of Chemical Physics, 132(15), 154104. [50] Vydrov, O. A., & van Voorhis, T. (2013). "Nonlocal van der Waals density functional: The rVV10 functional." Journal of Chemical Physics, 138(18), 184106. [51] Grimme, S. (2006). "Semiempirical GGA-type density functional constructed with a long-range dispersion correction." Journal of Computational Chemistry, 27(15), 1787-1799. [52] Becke, A. D. (1996). "Density-functional thermochemistry. III. The role of exact exchange." Journal of Chemical Physics, 104(3), 1040-1046. [53] Gritsenko, O. V., & van Leeuwen, R. (2002). "Meta-generalized gradient approximation to the exchange-correlation energy functional: Theoretical derivation and numerical tests." Journal of Chemical Physics, 117(11), 4662-4669. [54] Becke, A. D., & Dunning, T. H. (2002). "Density-functional thermochemistry. VI. The role of the correlation energy." Journal of Chemical Physics, 117(22), 10109-10117. [55] Sun, J., Ruzsinszky, A., & Perdew, J. P. (2015). "Strongly constrained and appropriately normed density functional for accurate description of molecules and solids." Physical Review Letters, 115(3), 036402. [56] Mardirossian, N., & Head-Gordon, M. (2009). "M06-L: A long-range-corrected hybrid functional for molecular simulations of nonmetals." Journal of Chemical Physics, 130(19), 194103. [57] Heyd, J., Scuseria, G. E., & Ernzerhof, M. (2003). "Hybrid functionals based on a screened Coulomb potential." Journal of Chemical Physics, 118(18), 8207-8215. [58] Mardirossian, N., & Head-Gordon, M. (2009). "M06-2X: A functional for multiple chemical contexts." Journal of Chemical Theory and Computation, 5(7), 1744-1761. [59] Adamo, C., & Barone, V. (1999). "A hybrid density functional for the determination of molecular structure and thermochemical properties." Journal of Chemical Physics, 110(13), 6158-6170. [60] Adamo, C., & Barone, V. (1999). "Tuning the density functional method for thermochemical applications: The B3LYP model." Journal of Chemical Physics, 110(13), 6158-6170. [61] Peng, Haowei, et al. "SCAN+ rVV10: A promising van der Waals density functional." arXiv preprint arXiv:1510.05712 (2015). [62] Carl L. Yaws , Ralph W. Pike.(2009). Thermophysical Properties of Chemicals and Hydrocarbons. Chapter 3 - Density of liquid—Organic compounds. [63] Anton Habenschuss, Elijah Johnson, A. H. Narten; X‐ray diffraction study and models of liquid methane at 92 K. J. Chem. Phys. 1 May 1981; 74 (9): 5234–5241. [64] Rapaport, D. C. The Art of Molecular Dynamics Simulation; Cambridge University Press: New York, 1995. [65] Jensen, F. Introduction to Computational Chemistry; Wiley: New York, 1999. [66] Straub, G.;Bassen, A.;Zweier, H.;Bertagnolli, H.;Todheide, K.;Soper, A. K.;Turner, J. Phys Rev E 1996, 53, 3505. [67] A. Kayode Coker(2007). ludwig's applied process design for chemical and petrochemical plants, volume 1, fourth edition. [68] Lisovskyi, K., & Rochenovich, G. D. (2023). Artificial Intelligence in the security system of enterprise. Grail of Science, (27), 308-316. [69] Belkacem, S. (2021). Machine learning approaches to rank news feed updates on social media (Doctoral dissertation, Université des Sciences et de la Technologie Houari Boumediene Alger). [70] Nejad, R. M., Sina, N., Moghadam, D. G., Branco, R., Macek, W., & Berto, F. (2022). Artificial neural network based fatigue life assessment of friction stir welding AA2024-T351 aluminum alloy and multi-objective optimization of welding parameters. International Journal of Fatigue, 160, 106840. [71] Machine learning intermolecular interaction energy calculationbased on functional-groups classify quantum chemistry database zhuo-xuan luo 2022 | - |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/96261 | - |
| dc.description.abstract | 本研究主要探討四面體甲烷二聚體分子之量子化學計算並將第一性原理分子動力學(Ab Initio Molecular Dynamics, AIMD)結合DeePMD-kit深度學習建構出勢能面模型並進行後續分子動力學模擬及其熱力學性質分析。
首先，在量子計算方面以密度泛函理論(Density Functional Theory, DFT)方法做計算，並針對甲烷分子間作用力類型篩選出了八種描述甲烷較理想的DFT交換相關泛函(Exchange-Correlation functional, XC functional)。而為了更貼近實際計算的情況以及瞭解有無使用DFT色散校正(Dispersion correction)的差別性與重要性，本此研究使用了AIMD贋勢基底(Pseudopotential basis set)在經由量子力學優化後的甲烷D3d二聚體構型上計算能量曲線，計算時中心原子距離從1.4 Å至18.5 Å共取14個點，並與CCSD(T)/CBS結果相比較，可較直觀地瞭解交換相關泛函和色散校正在贋勢基底選用上的差異。 而在分子動力學模擬方面利用CP2K軟體計算出各DFT交換相關泛函產生之AIMD軌跡數據作為DeePMD-kit深度學習的訓練資料。隨後透過深度學習建構出勢能面模型，並將此模型提取到LAMMPS軟體進行後續的分子動力學模擬，藉此得到了甲烷之徑向分佈函數(Radial Distribution Function, RDF)、均方位移(Mean Square Displacement, MSD)、速度自相關函數(Velocity AutoCorrelation Function, VACF)、擴散係數(Diffusion Coefficient, DC)以及剪力黏滯係數(Shear Viscosity Coefficient, SVC)等相關熱力學性質。最後，將結果與實驗值和經驗力場相互比較。 本研究結果表明，以AIMD和深度學習相結合後產生之力場模型，同時具有DFT之高精度，也具有基於經驗勢之高效率，為分子動力學模擬在精度與效率兩難之間，提供了一個全新的見解。 | zh_TW |
| dc.description.abstract | This study focuses on the quantum chemical calculation of tetrahedral methane dimer molecules and combines Ab Initio Molecular Dynamics (AIMD) with DeePMD-kit deep learning to construct a potential energy surface model for subsequent molecular dynamics simulations and thermal properties analysis.
Firstly, the DFT method is used in the quantum computation, and eight DFT Exchange-Correlation functionals are selected for the type of intermolecular forces to describe methane. In order to be closer to the actual calculation situation and to understand the difference and importance of using DFT dispersion correction or not, the AIMD Pseudopotential basis set was used to calculate the energy curves on the quantum mechanics optimized methane D3d dimer configuration, and the distance between the central atoms was taken from 1.4 Å to 18.5 Å at a total of 14 points. Comparing with the results of the CCSD(T)/CBS, the exchange-correlation functional, dispersion correction set can be understood more intuitively. In the molecular dynamics simulation, the AIMD trajectory data generated by DFT was calculated by CP2K software as the training data for DeePMD-kit deep learning. Then the force field model was constructed by deep learning, and this force field model was extracted to LAMMPS software for subsequent molecular dynamics simulation, so as to obtain the relevant thermodynamic properties of methane, such as RDF, MSD, VACF, DC, SVC, etc. Finally, the results are compared with experimental values and empirical force fields. The results of this study show that the force field model generated by combining AIMD and deep learning has both the high accuracy of DFT and the high efficiency based on the empirical potentials. All in all, deep learning has brought new insights to addressing the accuracy versus efficiency dilemma in molecular simulations. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2024-11-28T16:27:32Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2024-11-28T16:27:32Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | 口試委員會審定書 #
誌謝 i 摘要 ii ABSTRACT iii 目次 iv 圖次 viii 表次 xiii 第一章 緒論 1 1.1 研究動機 1 1.2 量子力學發展介紹 2 1.3 分子作用力介紹 3 1.4 計算分子作用力介紹 5 1.5 機器學習介紹 5 1.5.1 人工智慧、機器學習與深度學習之間的關係 6 1.5.2 機器學習類別 7 1.5.3 機器學習演算法 8 1.6 分子動力學模擬介紹 10 第二章 基本理論 11 2.1 量子力學理論 11 2.1.1 薛丁格方程式 11 2.1.2 波恩-歐本海默近似法 15 2.2 分子軌域理論 17 2.2.1 全初始法 18 2.2.2 自洽場理論 18 2.2.3 密度泛函理論 21 2.2.4 微擾理論 24 2.2.5 耦合簇理論 28 2.3 機器學習理論 29 2.3.1 神經網路 29 2.3.2 感知器 30 2.3.3 多層感知器 32 2.3.4 反向傳播 32 2.3.5 梯度消失 36 2.3.6 優化器 39 2.4 分子動力學理論 41 2.4.1 基本原理 41 2.4.2 週期性邊界條件 42 2.4.3 徑向分佈函數 43 2.4.4 均方位移 44 2.4.5 速度自相關函數 45 2.4.6 擴散係數 46 2.4.7 黏滯係數 47 第三章 計算方法 48 3.1 甲烷量子化學計算方法 49 3.1.1 甲烷二聚體能量計算 49 3.1.2 甲烷AIMD計算 50 3.2 DeePMD-kit模型計算方法 51 3.2.1 模型擴展性 52 3.2.2 原子中心框架 53 3.2.3 模型對稱性 54 3.2.4 描述符 56 3.2.5 深度神經網路 59 3.3 分子動力學模擬方法 61 第四章 模擬與計算結果 62 4.1 甲烷量子化學計算結果 62 4.1.1 甲烷二聚體不同色散校正能量計算結果 62 4.2 甲烷分子動力學模擬結果 70 4.2.1 徑向分佈函數模擬結果 70 4.2.2 均方位移模擬結果 95 4.2.3 速度自相關函數模擬結果 99 4.2.4 擴散係數模擬結果 103 4.2.5 剪力黏滯係數模擬結果 107 第五章 結論與展望 111 5.1 甲烷量子化學計算結論 111 5.2 甲烷分子動力學模擬結論 112 5.3 未來展望 113 參考文獻 114 | - |
| dc.language.iso | zh_TW | - |
| dc.subject | 密度泛函理論 | zh_TW |
| dc.subject | 剪力黏滯係數 | zh_TW |
| dc.subject | 擴散係數 | zh_TW |
| dc.subject | 速度自相關函數 | zh_TW |
| dc.subject | 均方位移 | zh_TW |
| dc.subject | 徑向分佈函數 | zh_TW |
| dc.subject | 分子動力學模擬 | zh_TW |
| dc.subject | 贋勢 | zh_TW |
| dc.subject | 深度學習 | zh_TW |
| dc.subject | 甲烷 | zh_TW |
| dc.subject | Shear Viscosity Coefficient(SVC) | en |
| dc.subject | Methane | en |
| dc.subject | Density Functional Theory(DFT) | en |
| dc.subject | Deep learning | en |
| dc.subject | Pseudopotential | en |
| dc.subject | Molecular Dynamics (MD) simulation | en |
| dc.subject | Radial Distribution Function(RDF) | en |
| dc.subject | Mean Square Displacement(MSD) | en |
| dc.subject | Velocity Autocorrelation Function(VAF) | en |
| dc.subject | Diffusion Coefficient (DC) | en |
| dc.title | 深度勢能分子動力學在甲烷中的應用 | zh_TW |
| dc.title | The application of Deep Potential Molecular Dynamics in methane | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 113-1 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | 李奕霈;游琇伃;楊延齡;李皇德 | zh_TW |
| dc.contributor.oralexamcommittee | Yi-Pei Li;Hsiu-Yu Yu;Yan-Ling Yang;Huang-Te Li | en |
| dc.subject.keyword | 甲烷,密度泛函理論,深度學習,贋勢,分子動力學模擬,徑向分佈函數,均方位移,速度自相關函數,擴散係數,剪力黏滯係數, | zh_TW |
| dc.subject.keyword | Methane,Density Functional Theory(DFT),Deep learning,Pseudopotential,Molecular Dynamics (MD) simulation,Radial Distribution Function(RDF),Mean Square Displacement(MSD),Velocity Autocorrelation Function(VAF),Diffusion Coefficient (DC),Shear Viscosity Coefficient(SVC), | en |
| dc.relation.page | 120 | - |
| dc.identifier.doi | 10.6342/NTU202404603 | - |
| dc.rights.note | 同意授權(限校園內公開) | - |
| dc.date.accepted | 2024-11-18 | - |
| dc.contributor.author-college | 工學院 | - |
| dc.contributor.author-dept | 應用力學研究所 | - |
| dc.date.embargo-lift | 2027-12-01 | - |
| 顯示於系所單位： | 應用力學研究所 | |
文件中的檔案：
| 檔案 | 大小 | 格式 | |
|---|---|---|---|
| ntu-113-1.pdf 未授權公開取用 | 4.99 MB | Adobe PDF | 檢視/開啟 |
系統中的文件，除了特別指名其著作權條款之外，均受到著作權保護，並且保留所有的權利。