[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118968-en":3,"doc-seo-118968-105":30,"detail-sidebar-cat-0-en-105":91},{"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":4,"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},118968,4810365810221,"Aurora","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Simulation Informed Machine Learning - Interpretation of Electrochemical Measurements - Dissertation","This dissertation addresses electrochemical interpretation by combining rigorous physics-based simulation with machine learning to overcome limitations created by multiphysics complexity and expansive parameter spaces. It targets two challenges where data needs and computational cost hinder mechanistic models and where degradation and time sensitivity complicate mechanistic diagnostics. For SOFC failure detection, physics simulations of fuel maldistribution, delamination, and oxidant crossover guided an SVM trained on synthetic 6-cell substack EIS data, achieving 90% accuracy across degradation and operating conditions. The second project uses a physics model to deconvolute edge effects for a reference electrode placed outside current paths, enabling accurate recovery of oxygen kinetic overpotential and validation against experiments across membrane thicknesses.","©Copyright 2023 Giang Tra Le  \nSimulation Informed Machine Learning Interpretation of Electrochemical Measurements  \nGiang Tra Le  \nA dissertation  \nsubmitted in partial fulfillment of the  \nrequirements for the degree of  \nDoctor of Philosophy  \nUniversity of Washington  \n2023  \nReading Committee:  \nStuart Adler, Chair  \nDavid A. C. Beck  \nDaniel T. Schwartz  \nProgram Authorized to Offer Degree:  \nChemical Engineering  \nUniversity of Washington  \nAbstract  \nSimulation Informed Machine Learning Interpretation of Electrochemical Measurements  \nGiang Tra Le  \nChair of the Supervisory Committee:  \nStuart Adler  \nChemical Engineering  \nThis work resides at the intersection of rigorous physics-based simulation and machine learning. We seek to address problems that have complicated multiphysics and exist in vast parameter spaces. Where the data requirement for machine learning methods can make the experimental burden untenable and the time-sensitive nature or high computational cost limits the practicality of mechanistic physics model. Our overarching objective is to explore solutions to bridge these limitations in electrochemistry research through the integration of machine learning and mechanistic simulation.  \nFailure detection in solid oxide fuel cell (SOFC) is complicated due to the need to disentangle the failure response from the effect of degradation-gradual change in performance with aging. We used physics models to simulate the behavior of SOFC under three failures that could occur during its operation: fuel maldistribution, delamination and oxidant gas crossover. These simulations revealed deviations in electrochemical impedance spectroscopy (EIS) from behavior of standard circuit elements under failures, underscoring the significance of physics-based modeling in SOFC diagnostics. Leveraging synthetic data of a 6-cell substack, we trained a support vector machine to identify failure modes with a 90% accuracy across degradation effects and operating conditions, discerning imperceptible differences in stack-level EIS responses. Investigation of synthetic data offered insights to failure diagnosis with EIS in determining most responsive frequency range and the efficacy of different  \nmachine learning methods.  \nIn the second project, we reexamined the utility of a reference electrode positioned outside the current path on a thin solid electrolyte. Extensive prior research with this design had demonstrated significant polarization shifts and half-cell EIS distortions from minor electrode misalignment, limiting its usefulness in quantitative assessment of two half-reactions. Employing a physics model to simulate these behaviors in a proton-exchange membrane electrolyzer, we trained a neural network model with simulated data to deconvolute edge effectsand determine the true oxygen kinetic overpotential with high accuracy (r2-score ¥ 0.96) . Validation with experimental data from electrolyzer cells of varying membrane thicknesses and misaligned electrodes confirmed the breakdown of oxygen and hydrogen evolution reaction losses to align with literature values. These findings unveil the potential use of this straightforward reference electrode design and intentional anode-cathode misalignment for evaluating individual electrode kinetics in performance or long-term degradation studies.  \nTABLE OF CONTENTS  \nPage  \nList of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii  \nChapter 1: Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1  \n1.1 Electrochemical Impedance Spectroscopy .................... 1  \n1.2 Physics based model of EIS ........................... 3  \n1.3 Machine learning application in electrochemistry . . . . . . . . . . . . . . . . 6  \n1.4 Combining mechanistic modeling and machine learning ............ 7  \nChapter 2: Simulation Informed Machine Learning Diagnostics of Solid Oxide Fuel Cell System ................................. 8  \n2. 1 Introduction . . . . . ","cbCaidVLhXkRsiH5","https://ap.wps.com/l/cbCaidVLhXkRsiH5","pdf",14972574,1,124,"English","en",105,"# Abstract\n# Chapter 1: Introduction\n## 1.1 Electrochemical Impedance Spectroscopy\n## 1.2 Physics based model of EIS\n## 1.3 Machine learning application in electrochemistry\n## 1.4 Combining mechanistic modeling and machine learning\n# Chapter 2: Simulation Informed Machine Learning Diagnostics of Solid Oxide Fuel Cell System\n## 2.1 Introduction\n## 2.2 Methodology\n## 2.3 Results and Discussion\n## 2.4 Conclusions\n## 2.5 Acknowledgments\n## 2.6 Appendix\n# Chapter 3: Machine reinterpretation of reference electrode measurement with intentional misalignment in proton-exchange membrane electrolyzer\n## 3.1 Introduction\n## 3.2 Experimental\n## 3.3 Physics Model\n## 3.4 Simulation Methodology\n## 3.5 Results and Discussion\n## 3.6 Conclusions\n## 3.7 Acknowledgement\n## 3.8 Supplementary\n## 3.9 Appendix\n# Chapter 4: Conclusion\n## 4.1 Summary\n## 4.2 Outlook","[{\"question\":\"How does the dissertation combine physics-based simulation with machine learning for electrochemistry?\",\"answer\":\"It integrates mechanistic physics simulations with machine learning so that synthetic or simulated behaviors can train models when experimental data requirements become impractical. The approach is tailored to electrochemical impedance spectroscopy interpretation and diagnostic tasks.\"},{\"question\":\"What failures are simulated and detected in the solid oxide fuel cell project?\",\"answer\":\"The study simulates three failure modes: fuel maldistribution, delamination, and oxidant gas crossover. It uses these physics-based simulations to train a support vector machine that identifies failure modes from EIS responses despite degradation effects.\"},{\"question\":\"How is the reference electrode measurement reinterpreted in the proton-exchange membrane electrolyzer project?\",\"answer\":\"A physics model is used to represent how electrode misalignment and edge effects distort measurements from a reference electrode outside the current path. A neural network trained on simulated data then deconvolutes these effects to recover the true oxygen kinetic overpotential with high accuracy.\"}]","Simulation Informed Machine Learning - Interpretation of Electrochemical Measurements - Dissertation | PDF",1785721259,312,{"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":86,"head_meta":88,"extra_data":90,"updated_unix":28},"simulation-informed-machine-learning-interpretation-of-electrochemical-measurements-dissertation","",{"@graph":36,"@context":85},[37,54,68],{"@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/simulation-informed-machine-learning-interpretation-of-electrochemical-measurements-dissertation/118968/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"How does the dissertation combine physics-based simulation with machine learning for electrochemistry?","Question",{"text":75,"@type":76},"It integrates mechanistic physics simulations with machine learning so that synthetic or simulated behaviors can train models when experimental data requirements become impractical. The approach is tailored to electrochemical impedance spectroscopy interpretation and diagnostic tasks.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What failures are simulated and detected in the solid oxide fuel cell project?",{"text":80,"@type":76},"The study simulates three failure modes: fuel maldistribution, delamination, and oxidant gas crossover. It uses these physics-based simulations to train a support vector machine that identifies failure modes from EIS responses despite degradation effects.",{"name":82,"@type":73,"acceptedAnswer":83},"How is the reference electrode measurement reinterpreted in the proton-exchange membrane electrolyzer project?",{"text":84,"@type":76},"A physics model is used to represent how electrode misalignment and edge effects distort measurements from a reference electrode outside the current path. 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