[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123880-en":3,"doc-seo-123880-105":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},123880,137441390410,"Hazel","https://ap-avatar.wpscdn.com/avatar/2000252f4ab5702993?_k=1776741390130283984",8,"Research & Report","Multi-Variable Multi-Metric Optimization of Self-Assembled Photocatalytic CO2 Reduction Performance Using Machine Learning Algorithms","The sunlight-driven reduction of CO2 into fuels and platform chemicals supports a circular economy, yet conventional optimization strategies fail for multivariable multimetric photocatalytic systems because they prioritize one performance metric at the expense of others. A metric is defined to quantify holistic system performance using multiple figures of merit, and machine learning is used to navigate a large experimental parameter matrix efficiently. A five-component self-assembled photocatalytic micelle platform is optimized to improve yield, quantum yield, turnover number, and frequency while keeping high selectivity. Data-driven analysis identifies buffer concentration as the dominant parameter, nearly four times more important than catalyst concentration, enabling standardized insights into performance bottlenecks.","POLITECNICO DI TORINO  \nRepository ISTITUZIONALE  \nMulti-Variable Multi-Metric Optimization of Self-Assembled Photocatalytic CO2 Reduction Performance Using Machine Learning Algorithms  \nOriginal  \nMulti-Variable Multi-Metric Optimization of Self-Assembled Photocatalytic CO2 Reduction Performance Using Machine Learning Algorithms / Bonke, Shannon A. ; Trezza, Giovanni; Bergamasco, Luca; Song, Hongwei; Rodríguez-Jiménez, Santiago; Hammarström, Leif; Chiavazzo, Eliodoro; Reisner, Erwin. -In: JOURNAL OF THE AMERICAN CHEMICAL SOCIETY. -ISSN 0002-7863. -146:(2024) . [10 . 1021/jacs.4c01305]  \nAvailability:  \nThis version is available at: 11583/2989335 since: 2024-06-05T10:07:53Z  \nPublisher: ACS  \nPublished  \nDOI:10.1021/jacs.4c01305  \nTerms of use:  \nThis article is made available under terms and conditions as specified in the corresponding bibliographic description in the repository  \nPublisher copyright  \n(Article begins on next page)  \n18 September 2024  \nThis article is licensed under CC-BY 4.0   \n[pubs.acs.org/JACS](pubs.acs.org/JACS)  Article   \nMulti-Variable Multi-Metric Optimization of Self-Assembled Photocatalytic CO2 Reduction Performance Using Machine Learning Algorithms  \nShannon A. Bonke, Giovanni Trezza, Luca Bergamasco, Hongwei Song, Santiago Rodríguez-Jiménez, Leif Hammarström, Eliodoro Chiavazzo, * and Erwin Reisner*  \n Cite This: J. Am. Chem. Soc. 2024, 146, 15648−15658  \nRead Online  \nDownloaded via 80.180.10 1.207 on June 5, 2024 at 09:58:14 (UTC) . See [https://pubs.acs.org/sharingguidelines](https://pubs.acs.org/sharingguidelines) for options on how to legitimately share published articles.  \nACCESS  \n Metrics & More  \n Article Recommendations  \n*sı   \nSupporting Information  \nABSTRACT: The sunlight-driven reduction of CO2 into fuels and platform chemicals is a promising approach to enable a circular economy. However, established optimization approaches are poorly suited to multivariable multimetric photocatalytic systems because they aim to optimize one performance metric while sacrificing the others and thereby limit overall system performance. Herein, we address this multimetric challenge by defining a metric for holistic system performance that takes multiple figures of merit into account, and employ a machine learning algorithm to efficiently guide our experiments through the large parameter matrix to make holistic optimization accessible for human experimentalists. As a test platform, we employ a five-component system that self-assembles into photocatalytic micelles for CO2-to-CO  \nreduction, which we experimentally optimized to simultaneously improve yield, quantum yield, turnover number, and frequency while maintaining high selectivity. Leveraging the data set with machine learning algorithms allows quantification of each parameter’s effect on overall system performance. The buffer concentration is unexpectedly revealed as the dominating parameter for optimal photocatalytic activity, and is nearly four times more important than the catalyst concentration. The expanded use and standardization of this methodology to define and optimize holistic performance will accelerate progress in different areas of catalysis by providing unprecedented insights into performance bottlenecks, enhancing comparability, and taking results beyond comparison of subjective figures of merit.  \n■ INTRODUCTION  \nCatalysis relies on a complex interplay of interdependent variables that must be optimized to meet a set of performance metrics. The challenge is exemplified by multicomponent photocatalytic systems where the parameter space is increasingly difficult to navigate due to the increasing number of variables required to provide supramolecular control (e.g., concentrations of reagents, additives, experimental variables). The optimization target is also unclear as there is limited standardization and a multitude of metrics to optimize (e.g., yield, quantum yield, selectivity, turnover number and frequency).1,2 Crucially, esta","cbCaioK55qeIMU0i","https://ap.wps.com/l/cbCaioK55qeIMU0i","pdf",3172692,1,12,"English","en",105,"# Abstract\n# Introduction","[{\"question\":\"Why are traditional optimization approaches unsuitable for multivariable multimetric photocatalytic systems?\",\"answer\":\"They typically optimize one metric while sacrificing others, preventing overall system performance from being improved holistically. Limited standardization and the variety of competing metrics further complicate optimization.\"},{\"question\":\"How does the document define “holistic” system performance?\",\"answer\":\"It defines a single objective function that mathematically connects multiple figures of merit into one scalar measure. Iteratively varying parameters allows the objective function to reflect overall performance.\"},{\"question\":\"What is revealed as the most important parameter for optimal photocatalytic activity?\",\"answer\":\"Buffer concentration is unexpectedly identified as the dominating parameter. It is nearly four times more important than catalyst concentration.\"}]","Multi-Variable Multi-Metric Optimization of Self-Assembled Photocatalytic CO2 Reduction Performance Using Machine Learning Algorithms | PDF",1785819045,30,{"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},"multi-variable-multi-metric-optimization-of-self-assembled-photocatalytic-co2-reduction-performance-using-machine-learning-algorithms","",{"@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/multi-variable-multi-metric-optimization-of-self-assembled-photocatalytic-co2-reduction-performance-using-machine-learning-algorithms/123880/",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","2026-08-04",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},"Why are traditional optimization approaches unsuitable for multivariable multimetric photocatalytic systems?","Question",{"text":75,"@type":76},"They typically optimize one metric while sacrificing others, preventing overall system performance from being improved holistically. Limited standardization and the variety of competing metrics further complicate optimization.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the document define “holistic” system performance?",{"text":80,"@type":76},"It defines a single objective function that mathematically connects multiple figures of merit into one scalar measure. Iteratively varying parameters allows the objective function to reflect overall performance.",{"name":82,"@type":73,"acceptedAnswer":83},"What is revealed as the most important parameter for optimal photocatalytic activity?",{"text":84,"@type":76},"Buffer concentration is unexpectedly identified as the dominating parameter. 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