以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：22

、訪客IP：3.141.47.139

姓名	鄭雅鴻(Ya-Hung Cheng)  查詢紙本館藏	畢業系所	化學工程與材料工程學系
論文名稱	使用有機分子的Sigma profile建立機器學習模型來預測金屬有機框架中的氣體吸附性質 (Machine Learning Models for Predicting Gaseous Adsorption in Metal-Organic Frameworks by Employing Sigma Profiles of Organic Linkers)
檔案	[Endnote RIS 格式]    [Bibtex 格式]    [相關文章]   [文章引用]   [完整記錄]   [館藏目錄]   至系統瀏覽論文 (2029-6-30以後開放)
摘要(中)	金屬有機框架多孔材料憑藉其優越的多孔性和功能性，被認為是極具潛力的氣體吸附劑。為了更有效率地篩選出最佳的氣體吸附劑，可以將分子模擬和機器學習結合，從而加速有潛力的多孔材料的開發。傳統上在機器學習中的多孔材料描述符主要依賴於該材料的結構和化性，這導致需要進一步運算每個多孔材料特性，然而由於金屬有機框架是模組化的結構，透過選擇不同的金屬節點和有機配體來建構多孔材料，因此多個金屬有機框架是共享相同的有機分子配體，並且能重複使用該資訊在不同多孔材料上。因此，此項研究發表了新的模組化的有機配體的表面電荷分佈描述符，表面電荷分佈稱為sigma profile，是使用導電類似溶劑模型進行生成，該模型結合了溶劑熱力學和計算量子力學，它關注於分子表面片段的概率分佈，考慮其特定的電荷密度。 這項研究結合分子模擬和機器學習，來預測碳氫氣體及酸性氣體兩種類型在金屬有機框架材料中的吸附特性。首先，利用蒙地卡羅Widom插入方法來計算亨利常數（KH），該常數表示金屬有機框架中碳氫類和酸性氣體的吸附強度。再者，建立了兩種類型機器學習模型包括隨機森林（RF）和極限梯度提升（XGBoost）來預測在3180個金屬有機框架材料中甲烷、乙烷、丙烷、乙烯、二氧化碳和硫化氫等氣體的吸附行為，由兩種方法確認較精準的學習模型，並利用此模型探討sigma profile描述符幫助模型性能的提升。本研究進一步隨機選擇100種由兩種有機配體組成的MOF對碳氫氣體進行模型測試，sigma profile的加入確實得到更高的預測精準度。此外，基於特徵重要性分析證實sigma profile模組化描述子確實起了相當重要的作用。最後，探討減少sigma profile特徵數量對預測精準度的影響顯示，誤差的增加可以忽略不計，整體效能依然穩定，也就是說即便使用較少的 sigma profile 特徵，也能在有限的計算資源下有效進行預測，也再次印證模組化描述符在金屬有機框架中的可用性。 總體而言，此項研究引入一種新穎的描述符，採用基於模組化的描述符特徵來構建精準的機器學習模型。這項研究的發現有望幫助未來識別用來各種吸附應用的多孔材料吸附劑。而本論文所構建的sigma profile數據庫可提供作為多孔材料領域的學者的寶貴資源。
摘要(英)	Metal-organic frameworks (MOFs) are considered highly promising gas adsorbents due to their superior porosity and functionality. To more efficiently screen for the best gas adsorbents, molecular simulation and machine learning can be combined to accelerate the development of potential porous materials. Traditionally, descriptors in machine learning for porous materials mainly rely on the structure and chemical properties of the material. This requires further calculations for each porous material. However, since MOFs are modular structures built by selecting different metal nodes and organic linkers, multiple MOFs share the same organic linkers, allowing the reuse of this information across different porous materials. This study introduces a new modular descriptor for the surface charge distribution of organic linker, known as the sigma profile. The sigma profile is generated using the conductor-like screening model, which combines solvent thermodynamics and computational quantum mechanics. It focuses on the probability distribution of molecular surface fragments, considering their specific charge densities. This study combines molecular simulation and machine learning to predict the adsorption characteristics of two types of gases, hydrocarbon and acid gases, in MOFs. First, the Henry’s coefficient (KH), which indicates the adsorption strength of hydrocarbon and acid gases in MOFs, is calculated using the Monte Carlo Widom insertion method. Next, two types of machine learning models, random forest (RF) and extreme gradient boosting (XGBoost), are established to predict the adsorption behaviors of methane, ethane, propane, ethylene, carbon dioxide, and hydrogen sulfide in 3180 MOF materials. The most accurate model is identified, and the enhancement of model performance by the sigma profile descriptor is explored. The study further tests the model on hydrocarbons in 100 MOFs composed of two types of organic linkers, demonstrating that the sigma profile significantly improves prediction accuracy. Feature importance analysis confirms the significant role of the sigma profile descriptor. Finally, the impact of reducing the number of sigma profile features on prediction accuracy is investigated, showing that the increase in error is negligible and overall performance remains stable. This indicates that even with fewer sigma profile features, effective predictions can be made with limited computational resources, reaffirming the utility of modular descriptors in MOFs. Overall, this study presents the sigma profile, a novel modular descriptor for predicting gas adsorption in MOFs using molecular simulation and machine learning. The sigma profile enhances prediction accuracy and efficiency, offering a valuable resource for future research.
關鍵字(中)	★ 金屬有機框架 ★ 機器學習 ★ 氣體吸附 ★ 分子模擬 ★ 量子力學	關鍵字(英)	★ Metal organic framework ★ Machine learning ★ Gas adsorption ★ Molecular simulation ★ Sigma profile
論文目次	中文摘要 i Abstract ii Acknowledgment iii Table of Contents iv List of Figures vi List of Table viii Chapter 1 Background and Motivation 1 1.1 Introduction 1 1.2 Literature Review on Computational MOF Research 6 1.3 Motivation and Scope of this Thesis 11 Chapter 2 Theory and Computational Details 12 2.1 Theory 12 2.1.1 Widom Particle Insertion Monte Carlo Method 12 2.1.2 Sigma Profile 16 2.1.3 Machine Learning Algorithms 19 2.2 Computational Details 21 2.2.1 Databases Collection 21 2.2.2 Adsorption Property Calculations 23 2.2.3 Sigma Profile 25 2.2.4 Traditional Descriptors 28 2.2.5 ML Model Construction 31 Chapter 3 Results and Discussion 33 3.1 Dataset and Henry’s Coefficient 34 3.2 Sigma Profile Descriptor 39 3.3 Hyperparameter tuning 40 3.4 Comparison of the ML Models 41 3.5 Prediction Performance of Hydrocarbon and Acid Gases 43 3.5.1 Hydrocarbon Gases 43 3.5.2 Acid Gases 56 3.6 Test on Diversity of MOFs 65 3.7 Feature Importance 68 3.8 Simplified Representation of Sigma Profiles 71 Chapter 4 Conclusion 74 Chapter 5 Future Work 75 Reference 76 Appendix 81 A. The number of organic linker types used in MOFs. 81 B. The occurrence frequency used organic linker types. 82 C. Lennard-Jones parameters. 98 D. Comparison of RF and XGBoost machine learning assessment methods for hydrogen sulfide. Each point is color-coded to indicate the data density. 101 E. The metrics values using varying combination features as model inputs for the adsorption of hydrocarbon molecules and acid gases in MOFs. 102 F. The metrics values using varying combination features as model inputs for the adsorption of hydrocarbon molecules and acid gases in MOFs. 104 G. The metrics values using XGBoost ML models trained with different σ-profile representations from without σ-profiles (Nσ = 0), reduced σ-profiles (Nσ = 6, 11, 26), to full σ-profiles (Nσ = 51) for the adsorption of hydrocarbon molecules and acid gases in MOFs. 106
參考文獻	[1] W John Thomas F, Crittenden B. Adsorption technology and design. Butterworth-Heinemann; 1998. [2] Wu Y, Weckhuysen BM. Separation and purification of hydrocarbons with porous materials. Angew Chem Int Ed 2021; 60: 18930-49. https://doi.org/10.1002/anie.202104318. [3] Mango FD. The light hydrocarbons in petroleum: A critical review. Org Geochem 1997; 26: 417-40. https://doi.org/10.1016/S0146-6380(97)00031-4. [4] AYDIN H, İLKILIÇ C. Air pollution, pollutant emissions and harmful effects. J Eng Technol 2017; 1: 8-15. https://doi.org/en/pub/jetech/issue/33471/356673. [5] Bamdad H, Hawboldt K, MacQuarrie S. A review on common adsorbents for acid gases removal: Focus on biochar. Renew Sust Energ Rev 2018; 81: 1705-20. https://doi.org/10.1016/j.rser.2017.05.261. [6] Florides GA, Christodoulides P. Global warming and carbon dioxide through sciences. Environ Int 2009; 35: 390-401. https://doi.org/10.1016/j.envint.2008.07.007. [7] Saeedi A, Najibi A, Mohammadi-Bardbori A. Effects of long-term exposure to hydrogen sulfide on human red blood cells. J Occup Environ Med 2015; 6: 20. https://doi.org/10.15171/ijoem.2015.482. [8] Brunauer S, Emmett PH, Teller E. Adsorption of gases in multimolecular layers. J Am Chem Soc 1938; 60: 309-19. [9] Wikipedia c, BET theory, in. [10] Sircar S. Basic research needs for design of adsorptive gas separation processes. Ind Eng Chem Res 2006; 45: 5435-48. https://doi.org/10.1021/ie051056a. [11] Yang RT. Adsorbents: Fundamentals and applications. John Wiley & Sons; 2003. [12] Menéndez-Díaz J, Martín-Gullón I. Types of carbon adsorbents and their production. In: Interface science and technology: Elsevier; 2006, p. 1-47. [13] Wikipedia c, Adsorption, in. [14] Sabzehmeidani MM, Mahnaee S, Ghaedi M, Heidari H, Roy VA. Carbon based materials: A review of adsorbents for inorganic and organic compounds. Materials Advances 2021; 2: 598-627. https://doi.org/10.1039/D0MA00087F. [15] IndustryARC updated the market research study on “adsorbents Market” - Forecast (2024 - 2030), https://www.linkedin.com/pulse/industryarc-updated-market-research-study-adsorbents-manikanta-b-cy0wc [16] Crom A, Holley K, Feldblyum J. Implementation of high school level laboratory experiments demonstrating nanoscale porosity in metal–organic frameworks. 2023. https://doi.org/10.26434/chemrxiv-2023-9tmfh. [17] Zhou H-C, Long JR, Yaghi OM, Introduction to metal–organic frameworks, in, ACS Publications, 2012, pp. 673-4. [18] Furukawa H, Cordova KE, O’Keeffe M, Yaghi OM. The chemistry and applications of metal-organic frameworks. Science 2013; 341: 1230444. https://doi.org/10.1126/science.1230444. [19] Yaghi OM, O′Keeffe M, Ockwig NW, Chae HK, Eddaoudi M, Kim J. Reticular synthesis and the design of new materials. Nature 2003; 423: 705-14. https://doi.org/10.1038/nature01650. [20] Eddaoudi M, Kim J, Rosi N, Vodak D, Wachter J, O′Keeffe M, et al. Systematic design of pore size and functionality in isoreticular MOFs and their application in methane storage. Science 2002; 295: 469-72. https://doi.org/10.1126/science.1067208. [21] Albolkany MK, Liu C, Wang Y, Chen CH, Zhu C, Chen X, et al. Molecular surgery at microporous MOF for mesopore generation and renovation. Angew Chem Int Ed 2021; 60: 14601-8. https://doi.org/10.1002/anie.202103104. [22] Vermoortele F, Ameloot R, Alaerts L, Matthessen R, Carlier B, Fernandez EVR, et al. Tuning the catalytic performance of metal–organic frameworks in fine chemistry by active site engineering. Journal of Materials Chemistry 2012; 22: 10313-21. https://doi.org/10.1039/C2JM16030G. [23] Aggarwal V, Solanki S, Malhotra BD. Applications of metal–organic framework-based bioelectrodes. Chem Sci 2022; 13: 8727-43. https://doi.org/10.1039/D2SC03441G. [24] Ahmad M, Luo Y, Wöll C, Tsotsalas M, Schug A. Design of metal-organic framework templated materials using high-throughput computational screening. Molecules 2020; 25: 4875. https://doi.org/10.3390/molecules25214875 [25] Shahzad K, Mardare AI, Hassel AW. Accelerating materials discovery: combinatorial synthesis, high-throughput characterization, and computational advances. Science and Technology of Advanced Materials: Methods 2024; 4: 2292486. https://doi.org/10.1080/27660400.2023.2292486. [26] Xu D, Zhang Q, Huo X, Wang Y, Yang M. Advances in data‐assisted high‐throughput computations for material design. Materials Genome Engineering Advances 2023; 1: e11. https://doi.org/10.1002/mgea.11. [27] Cho EH, Lin L-C. Nanoporous material recognition via 3D convolutional neural networks: Prediction of adsorption properties. The journal of physical chemistry letters 2021; 12: 2279-85. https://doi.org/10.1021/acs.jpclett.1c00293. [28] Sakamaki Y, Tsuji M, Heidrick Z, Watson O, Durchman J, Salmon C, et al. Preparation and applications of metal-organic frameworks (MOFs): A laboratory activity and demonstration for high school and/or undergraduate students. Journal of chemical education 2020; 97: 1109-16. https://doi.org/10.1021/acs.jchemed.9b01166. [29] Chung YG, Haldoupis E, Bucior BJ, Haranczyk M, Lee S, Zhang H, et al. Advances, updates, and analytics for the computation-ready, experimental metal–organic framework database: CoRE MOF 2019. J Chem Eng Data 2019; 64: 5985-98. https://doi.org/10.1021/acs.jced.9b00835. [30] Wikipedia c, Molecular modelling, in. [31] Barbhuiya S, Das BB. Molecular dynamics simulation in concrete research: a systematic review of techniques, models and future directions. Journal of Building Engineering 2023; 107267. https://doi.org/10.1016/j.jobe.2023.107267. [32] Jose R, Bangar G, Pal S, Rajaraman G. Role of molecular modelling in the development of metal-organic framework for gas adsorption applications. Journal of Chemical Sciences 2023; 135: 19. [33] 刘海莲, 刘姝, 任瑞霞, 宋雯雯. 分子模拟技术在分子筛的吸附扩散研究中的应用. 化学与粘合 2011; 33: 60-4. [34] Jordan MI, Mitchell TM. Machine learning: Trends, perspectives, and prospects. Science 2015; 349: 255-60. https://doi.org/10.1126/science.aaa8415. [35] Machine Learning and deep Learning in computational toxicology (Book). [36] Demir H, Daglar H, Gulbalkan HC, Aksu GO, Keskin S. Recent advances in computational modeling of MOFs: From molecular simulations to machine learning. Coord Chem Rev 2023; 484: 215112. https://doi.org/10.1016/j.ccr.2023.215112. [37] Moghadam PZ, Rogge SM, Li A, Chow C-M, Wieme J, Moharrami N, et al. Structure-mechanical stability relations of metal-organic frameworks via machine learning. Matter 2019; 1: 219-34. https://doi.org/10.1016/j.matt.2019.03.002. [38] Dai D, Liu Q, Hu R, Wei X, Ding G, Xu B, et al. Method construction of structure-property relationships from data by machine learning assisted mining for materials design applications. Materials & Design 2020; 196: 109194. https://doi.org/10.1016/j.matdes.2020.109194. [39] Chong S, Lee S, Kim B, Kim J. Applications of machine learning in metal-organic frameworks. Coord Chem Rev 2020; 423. https://doi.org/10.1016/j.ccr.2020.213487. [40] Fanourgakis GS, Gkagkas K, Tylianakis E, Froudakis GE. A universal machine learning algorithm for large-scale screening of materials. J Am Chem Soc 2020; 142: 3814-22. https://doi.org/10.1021/jacs.9b11084. [41] Sung I-T, Lin L-C. In silico study of metal–organic frameworks for CO2/CO separation: Molecular simulations and machine learning. J Phys Chem C 2023; 127: 13886-99. https://doi.org/10.1021/acs.jpcc.3c02452. [42] Halder P, Singh JK. High-throughput screening of metal–organic frameworks for ethane–ethylene separation using the machine learning technique. Energy & Fuels 2020; 34: 14591-7. https://doi.org/10.1021/acs.energyfuels.0c03063. [43] Pardakhti M, Moharreri E, Wanik D, Suib SL, Srivastava R. Machine learning using combined structural and chemical descriptors for prediction of methane adsorption performance of metal organic frameworks (MOFs). ACS Comb Sci 2017; 19: 640-5. https://doi.org/10.1021/acscombsci.7b00056. [44] Butler KT, Frost JM, Skelton JM, Svane KL, Walsh A. Computational materials design of crystalline solids. Chem Soc Rev 2016; 45: 6138-46. https://doi.org/10.1039/C5CS00841G. [45] Bucior BJ, Bobbitt NS, Islamoglu T, Goswami S, Gopalan A, Yildirim T, et al. Energy-based descriptors to rapidly predict hydrogen storage in metal–organic frameworks. Mol Syst Des Eng 2019; 4: 162-74. https://doi.org/10.1039/C8ME00050F. [46] Anderson R, Rodgers J, Argueta E, Biong A, Gómez-Gualdrón DA. Role of pore chemistry and topology in the CO2 capture capabilities of MOFs: From molecular simulation to machine learning. Chem Mater 2018; 30: 6325-37. https://doi.org/10.1021/acs.chemmater.8b02257. [47] Fernandez M, Trefiak NR, Woo TK. Atomic property weighted radial distribution functions descriptors of metal–organic frameworks for the prediction of gas uptake capacity. J Phys Chem C 2013; 117: 14095-105. https://doi.org/10.1021/jp404287t. [48] Hung T-H, Xu Z-X, Kang D-Y, Lin L-C. Chemistry-encoded convolutional neural networks for predicting gaseous adsorption in porous materials. J Phys Chem C 2022; 126: 2813-22. https://doi.org/10.1021/acs.jpcc.1c09649. [49] Frenkel D, Smit B. Understanding molecular simulation: from algorithms to applications. Elsevier; 2023. [50] Widom B. Some topics in the theory of fluids. The Journal of Chemical Physics 1963; 39: 2808-12. https://doi.org/10.1063/1.1734110. [51] Dubbeldam D, Calero S, Ellis DE, Snurr RQ. Raspa: Molecular simulation software for adsorption and diffusion in flexible nanoporous materials. Mol Simulat 2016; 42: 81-101. https://doi.org/10.1080/08927022.2015.1010082. [52] Lin S-T, Sandler SI. A priori phase equilibrium prediction from a segment contribution solvation model. Ind Eng Chem Res 2002; 41: 899-913. https://doi.org/10.1021/ie001047w. [53] Klamt A, Schüürmann G. COSMO: A new approach to dielectric screening in solvents with explicit expressions for the screening energy and its gradient. J Chem Soc Perk Trans 2 1993; 799–805. https://doi.org/10.1039/P29930000799. [54] Klamt A. Conductor-like screening model for real solvents: A new approach to the quantitative calculation of solvation phenomena. J Phys Chem 1995; 99: 2224-35. https://doi.org/10.1021/J100007A062. [55] Hsieh C-M, Sandler SI, Lin S-T. Improvements of COSMO-SAC for vapor–liquid and liquid–liquid equilibrium predictions. Fluid Phase Equilib 2010; 297: 90-7. https://doi.org/10.1016/j.fluid.2010.06.011. [56] Chen T, Guestrin C. Xgboost: A scalable tree boosting system. Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining 2016; 785-94. https://doi.org/10.1145/2939672.2939785. [57] Moghadam PZ, Li A, Liu X-W, Bueno-Perez R, Wang S-D, Wiggin SB, et al. Targeted classification of metal–organic frameworks in the Cambridge structural database (CSD). Chem Sci 2020; 11: 8373-87. https://doi.org/10.1039/D0SC01297A. [58] Bucior BJ, Rosen AS, Haranczyk M, Yao Z, Ziebel ME, Farha OK, et al. Identification schemes for metal–organic frameworks to enable rapid search and cheminformatics analysis. Cryst Growth Des 2019; 19: 6682-97. https://doi.org/10.1021/acs.cgd.9b01050. [59] Zou C, Penley DR, Cho EH, Lin L-C. Efficient and accurate charge assignments via a multilayer connectivity-based atom contribution (m-CBAC) approach. J Phys Chem C 2020; 124: 11428-37. https://doi.org/10.1021/acs.jpcc.0c01524. [60] Cho EH, Lin L-C. Electrostatic potential optimized molecular models for molecular simulations: CO, CO2, COS, H2S, N2, N2O, and SO2. J Chem Theory Comput 2019; 15: 6323-32. https://doi.org/10.1021/acs.jctc.9b00653. [61] Martin MG, Siepmann JI. Transferable potentials for phase equilibria. 1. United-atom description of n-alkanes. J Phys Chem B 1998; 102: 2569-77. https://doi.org/10.1021/jp972543+. [62] Wick CD, Martin MG, Siepmann JI. Transferable potentials for phase equilibria. 4. United-atom description of linear and branched alkenes and alkylbenzenes. J Phys Chem B 2000; 104: 8008-16. https://doi.org/10.1021/jp001044x. [63] Wang Y, Xiao J, Suzek TO, Zhang J, Wang J, Bryant SH. PubChem: a public information system for analyzing bioactivities of small molecules. Nucleic Acids Res 2009; 37: W623-W33. https://doi.org/10.1093/nar/gkp456. [64] Te Velde Gt, Bickelhaupt FM, Baerends EJ, Fonseca Guerra C, van Gisbergen SJ, Snijders JG, et al. Chemistry with ADF. J Comput Chem 2001; 22: 931-67. https://doi.org/10.1002/jcc.1056. [65] Perdew JP. Erratum: Density-functional approximation for the correlation energy of the inhomogeneous electron gas. Phys Rev B 1986; 34: 7406. https://doi.org/10.1103/PhysRevB.34.7406. [66] Becke AD. Density-functional exchange-energy approximation with correct asymptotic behavior. Phys Rev A 1988; 38: 3098. https://doi.org/10.1103/PhysRevA.38.3098. [67] Paulechka E, Diky V, Kazakov A, Kroenlein K, Frenkel M. Reparameterization of COSMO-SAC for phase equilibrium properties based on critically evaluated data. Journal of Chemical Engineering Data 2015; 60: 3554-61. [68] Bell IH, Mickoleit E, Hsieh C-M, Lin S-T, Vrabec J, Breitkopf C, et al. A benchmark open-source implementation of COSMO-SAC. J Chem Theory Comput 2020; 16: 2635-46. https://doi.org/10.1021/acs.jctc.9b01016. [69] Willems TF, Rycroft CH, Kazi M, Meza JC, Haranczyk M. Algorithms and tools for high-throughput geometry-based analysis of crystalline porous materials. Microporous Mesoporous Mater 2012; 149: 134-41. https://doi.org/10.1016/j.micromeso.2011.08.020. [70] Yang C, Qi J, Wang A, Zha J, Liu C, Yao S. Application of machine learning in MOFs for gas adsorption and separation. Mater Res Express 2023; 10: 122001. https://doi.org/10.1088/2053-1591/ad0c07. [71] Uzun A, Keskin S. Site characteristics in metal organic frameworks for gas adsorption. Prog Surf Sci 2014; 89: 56-79. https://doi.org/10.1016/j.progsurf.2013.11.001. [72] Wang C, Luo H, Li H, Zhu X, Yu B, Dai S. Tuning the physicochemical properties of diverse phenolic ionic liquids for equimolar CO2 capture by the substituent on the anion. Eur J Chem 2012; 18: 2153-60. https://doi.org/10.1002/chem.201103092. [73] Saha D, Kienbaum MJ. Role of oxygen, nitrogen and sulfur functionalities on the surface of nanoporous carbons in CO2 adsorption: a critical review. Microporous Mesoporous Mater 2019; 287: 29-55. https://doi.org/10.1016/j.micromeso.2019.05.051.
指導教授	謝介銘 林立強(Chieh-Ming Hsieh Li-Chiang Lin)	審核日期	2024-7-23
推文	facebook   plurk   twitter   funp   google   live   udn   HD   myshare   reddit   netvibes   friend   youpush   delicious   baidu
網路書籤	Google bookmarks   del.icio.us   hemidemi   myshare