[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124291-en":3,"doc-seo-124291-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},124291,1374391974468,"Eden","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","ACCELERATING THE EXPLORATION OF CARBOXYLIC ACID-ZEOLITE INTERACTIONS - USING CLASSICAL AND MACHINE LEARNING INTERATOMIC POTENTIALS - Thesis","The upgrading of carboxylic acids over zeolitic catalysts is a promising path toward a sustainable future. This study systematically examines interactions between various carboxylic acids and Brønsted acid sites in zeolite frameworks. Classical interatomic potential simulations, machine learning interatomic potentials trained with NequIP, and density functional theory calculations are combined to predict adsorption configurations and adsorption energies. The MLIP models improve accuracy, while transferability limitations remain, highlighting both capability and constraints of computational catalyst design. Future work is directed toward more transferable ML models.","ACCELERATING THE EXPLORATION OF CARBOXYLIC ACID-ZEOLITE INTERACTIONS:  \nUSING CLASSICAL AND MACHINE LEARNING INTERATOMIC POTENTIALS  \nby  \nAmitabh Roy  \nA thesis submitted to The Johns Hopkins University in conformity with the requirements for the degree of Master of Science in Engineering  \nBaltimore, Maryland  \nMay 2025  \n© 2025 Amitabh Roy  \nAll rights reserved  \nAbstract  \nThe upgrading of carboxylic acids over zeolitic catalysts is a promising path to a sustainable future. In this study, we systematically investigated the interactions between various carboxylic acids and Brønsted acid sites within zeolite frameworks. A combination of classical interatomic potentials simulations, machine learning interatomic potentials (MLIPs), and density functional theory (DFT) calculations was employed to predict adsorption configurations and energies. MLIPs trained using NequIP improved accuracy, although challenges remained in transferability. This work highlights both the potential and current limitations of computational modeling in catalyst design, suggesting future improvements through more transferable ML models.  \nKeywords: DFT (Density Functional Theory), MC (Monte Carlo), MD (Molecular Dynamics), Force fields (FF), Machine Learning Interatomic Potentials (MLIPs), SAF (Synthetic Aviation Fuel/Sustainable Aviation Fuel), Brønsted Acid Site (BAS)  \nPrimary reader and thesis advisor  \nDr. Brandon C. Bukowski Assistant Professor  \nDepartment of Chemical And Bimolecular Engineering Johns Hopkins University, Baltimore MD  \nSecondary reader  \nDr. Paulette Clancy  \nEdward J. Schaefer Professor  \nDepartment of Chemical And Bimolecular Engineering Johns Hopkins University, Baltimore MD  \nAcknowledgement  \nFrom the beginning of my graduate studies, I aspired to understand catalysis atthe atomic scale and contribute to research with impact on a sustainable future. I am deeply grateful to Dr. Brandon C. Bukowski for supporting that vision and providing me with the mentorship and opportunities that shaped this work. I extend my sincere thanks to Mingze Zheng and Yanqi Huang for their invaluable guidance in computational methods, from configuring VASP simulations to training NequIP models.  \nTo my lab colleagues: your thoughtful critiques during group meetings played a crucial role in refining this work. I am especially thankful to Dr. Paulette Clancy, whose feedback during my presentations pushed me to refine both my scientific reasoning and communication. Her insights were particularly helpful in refining the motivation and conclusions of this thesis.  \nFinally, none of this would have been possible without the unwavering faith, encouragement, and sacrifices of my parents.  \nTable of Contents  \nAbstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ii  \nAcknowledgement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iv  \nList of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii  \nList of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . viii  \nChapter 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . 1  \n1.1 Current SAF Landscape and Challenges ................ 1  \n1.2 Carboxylic acid pathway ......................... 2  \n1.3 Catalytic Upgrading with Zeolites .................... 3  \n1.4 Scope of this Thesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8  \nChapter 2 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . 10  \n2.1 Monte Carlo (MC) ............................ 11  \n2.2 Density Functional Theory (DFT) .................... 14  \n2.3 Machine Learning Interatomic Potentials (MLIPs) ........... 15  \n2.4 Critical Diameter Estimation . . . . . . . . . . . . . . . . . . . . . . 16  \n2.5 Materials Informatics . . . . . . . . . . . . . . . . . . . . . . . . . . . 17  \nChapter 3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20  \n3. 1 Size Matching Between Molecules and Zeolite Fr","cbCaieqpc9aFcdvE","https://ap.wps.com/l/cbCaieqpc9aFcdvE","pdf",16364055,1,58,"English","en",105,"# Abstract\n# Acknowledgement\n# List of Tables\n# List of Figures\n# Chapter 1 Introduction\n## Current SAF Landscape and Challenges\n## Carboxylic acid pathway\n## Catalytic Upgrading with Zeolites\n## Scope of this Thesis\n# Chapter 2 Methodology\n## Monte Carlo (MC)\n## Density Functional Theory (DFT)\n## Machine Learning Interatomic Potentials (MLIPs)\n## Critical Diameter Estimation\n## Materials Informatics\n# Chapter 3 Results\n## Size Matching Between Molecules and Zeolite Frameworks\n## Monte Carlo Simulations for Molecule-Zeolite Interactions\n## Training Machine Learning Interatomic Potentials (MLIPs)\n## Force and Energy Predictions from MLIPs and DFT\n## Adsorption Energy Calculations for Selected Acids\n# Chapter 4 Discussion\n# Chapter 5 Conclusion\n# Appendix A Dataset and Methodological Details\n## Classicial Force Field Parameters\n## Collective Variable Definition\n# Appendix B Supplementary Results and Validation\n## Molecule size and pore dimension comparison\n## VASP Optimizer Testing\n## Monte Carlo Results for FAU\n## Modified BKS Force Field Parameters","[{\"question\":\"What research problem does the thesis address?\",\"answer\":\"The thesis investigates how carboxylic acids interact with Brønsted acid sites in zeolite frameworks to support catalytic upgrading pathways for a more sustainable future.\"},{\"question\":\"Which computational methods are used to study the interactions?\",\"answer\":\"Classical interatomic potential simulations, machine learning interatomic potentials (MLIPs) trained with NequIP, and density functional theory (DFT) calculations are used together to predict adsorption structures and energies.\"},{\"question\":\"How well do the MLIP models perform, and what limitation remains?\",\"answer\":\"NequIP-trained MLIPs improve prediction accuracy, but transferability challenges remain when applying the learned model to new conditions.\"}]","ACCELERATING THE EXPLORATION OF CARBOXYLIC ACID-ZEOLITE INTERACTIONS - USING CLASSICAL AND MACHINE LEARNING INTERATOMIC POTENTIALS - Thesis | PDF",1785821412,146,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":28},"accelerating-the-exploration-of-carboxylic-acid-zeolite-interactions-using-classical-and-machine-learning-interatomic-potentials-thesis","",{"@graph":36,"@context":86},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/accelerating-the-exploration-of-carboxylic-acid-zeolite-interactions-using-classical-and-machine-learning-interatomic-potentials-thesis/124291/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05","2026-08-04",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What research problem does the thesis address?","Question",{"text":76,"@type":77},"The thesis investigates how carboxylic acids interact with Brønsted acid sites in zeolite frameworks to support catalytic upgrading pathways for a more sustainable future.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Which computational methods are used to study the interactions?",{"text":81,"@type":77},"Classical interatomic potential simulations, machine learning interatomic potentials (MLIPs) trained with NequIP, and density functional theory (DFT) calculations are used together to predict adsorption structures and energies.",{"name":83,"@type":74,"acceptedAnswer":84},"How well do the MLIP models perform, and what limitation remains?",{"text":85,"@type":77},"NequIP-trained MLIPs improve prediction accuracy, but transferability challenges remain when applying the learned model to new conditions.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":46,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":46,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":46,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]