[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127003-en":3,"doc-seo-127003-105":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},127003,2336474466412,"Ezra","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Machine Learning Models for Accurately Predicting Properties of CsPbCl3 Perovskite Quantum Dots - Research summary","Perovskite Quantum Dots (PQDs) show strong potential for multiple applications due to their distinctive optical and electronic behavior. This study evaluates machine learning models to predict the size, absorbance (1S abs), and photoluminescence (PL) properties of CsPbCl3 PQDs using synthesizing features as the input dataset. Support Vector Regression, Nearest Neighbour Distance, Random Forest, Gradient Boosting Machine, Decision Tree, and Deep Learning are trained and assessed, with SVR and NND delivering the best prediction quality via high R2 and low RMSE/MAE.","arXiv :2406 . 15515v1 [ cond-mat .mtrl-sci ] 20 Jun 2024  \nMachine Learning Models for Accurately Predicting Properties of CsPbCl3 Perovskite  \nQuantum Dots Mehmet Sıddık C¸adırcı 1 ∗ , Musa C¸adırcı2  \n1 Faculty of Science, Department of Statistics, Cumhuriyet University, Sivas, Turkey.  \n2 Department of Electrical & Electronics Engineering, Duzce University, D¨uzce, Turkey.  \n∗ Corresponding author, [Email:](Email:msiddikcadirci@cumhuriyet.edu.tr)[msiddikcadirci@cumhuriyet.edu.tr](Email:msiddikcadirci@cumhuriyet.edu.tr)  \nAbstract  \nPerovskite Quantum Dots (PQDs) have a promising future for several applications due to their unique properties. This study investigates the effectiveness of Machine Learning (ML) in predicting the size, absorbance (1S abs) and photoluminescence (PL) properties of CsPbCl3 PQDs using synthesizing features as the input dataset. the study employed ML models of Support Vector Regression (SVR), Nearest Neighbour Distance (NND), Random Forest (RF), Gradient Boosting Machine (GBM), Decision Tree (DT) and Deep Learning (DL) . Although all models performed highly accurate results, SVR and NND demonstrated the best accurate property prediction by achieving excellent performance on the test and training datasets, with high R2 and low Root Mean Squared Error (RMSE) and low Mean Absolute Error (MAE) metric values. Given that ML is becoming more superior, its ability to understand the QDs field could prove invaluable to shape the future of nanomaterials designing.  \n1 INTRODUCTION  \nWithin computer science, artificial intelligence (AI) refers to the study of designing intelligent machines which can sense the environment around them and take appropriate actions accordingly [15] . Machine learning (ML) is a branch of artificial intelligence that employs algorithms to develop mathematical models from data for solving specific problems directly without applying physical principles that gave birth to the data. This method is especially valuable when the connection between the variables used in the study and the results of the study is not understood. Widespread access to computational power along with the increasing amount of data available for experimentation has resulted in the emergence and application in different areas of science and industry of advanced machine learning models [8, 19] . Thanks to the capability of evaluating the massive amount of data, Machine learning (ML) is significant for predicting  \nQDs’ properties with a high accuracy. Since QDs’ properties are highly dependent on the size, and composition[4], ML algorithms are appropriate tools to handle the data and exhibit well-expressed interactions between the input variables and the resultant properties. Moreover, ML can improve the procedure of synthesizing QDs in order to give out desired characteristics without running more expensive tests and complex simulations which take much time[20] . Besides, ML can unearth hidden patterns from data that aid scientists in understanding new mechanisms and links in QDs[17][23] . Predicting properties of QDs in diverse conditions using ML is important in materials design for specific applications[20] .  \nAll inorganic metal halide Perovskite Quantum Dots (PQDs) have shown great promise due to their unique optical and electronic properties. They exhibit sizeand composition-dependent properties and are cubic in shape, with the size ranging from approximately 3 nm to 15 nm. Therefore, PQDs offer band gap tunability over a wide range and property tailoring capabilities. Compared to their counterparts, PQDs have high photoluminescence quantum yield, narrow emission linewidth, higher stability, higher charge mobility, and longer diffusion length [5][24][22][2][21] . These properties make them exceptionally critical in a wide range of applications; including solar cells[26], lasers[21], LEDs[6], and medical imaging[16] . PQDs are produced using the colloidal synthesizing method at high temperatures where the temper","cbCaibD0X0dt3lfM","https://ap.wps.com/l/cbCaibD0X0dt3lfM","pdf",882712,1,23,"English","en",105,"# Abstract\n# Introduction\n# Methodology\n## Data Description","[{\"question\":\"Which machine learning models were used to predict CsPbCl3 PQD properties?\",\"answer\":\"The study used Support Vector Regression (SVR), Nearest Neighbour Distance (NND), Random Forest (RF), Gradient Boosting Machine (GBM), Decision Tree (DT), and Deep Learning (DL).\"},{\"question\":\"What properties of CsPbCl3 perovskite quantum dots were predicted?\",\"answer\":\"The models predicted PQD size, absorbance (1S abs), and photoluminescence (PL) properties.\"},{\"question\":\"How did the best-performing models compare in evaluation metrics?\",\"answer\":\"SVR and NND achieved the best results, showing high R2 with low Root Mean Squared Error (RMSE) and low Mean Absolute Error (MAE) on both test and training datasets.\"}]","Machine Learning Models for Accurately Predicting Properties of CsPbCl3 Perovskite Quantum Dots - Research summary | PDF",1785936220,58,{"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},"machine-learning-models-for-accurately-predicting-properties-of-cspbcl3-perovskite-quantum-dots-research-summary","",{"@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/machine-learning-models-for-accurately-predicting-properties-of-cspbcl3-perovskite-quantum-dots-research-summary/127003/",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","2026-08-05",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},"Which machine learning models were used to predict CsPbCl3 PQD properties?","Question",{"text":75,"@type":76},"The study used Support Vector Regression (SVR), Nearest Neighbour Distance (NND), Random Forest (RF), Gradient Boosting Machine (GBM), Decision Tree (DT), and Deep Learning (DL).","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What properties of CsPbCl3 perovskite quantum dots were predicted?",{"text":80,"@type":76},"The models predicted PQD size, absorbance (1S abs), and photoluminescence (PL) properties.",{"name":82,"@type":73,"acceptedAnswer":83},"How did the best-performing models compare in evaluation metrics?",{"text":84,"@type":76},"SVR and NND achieved the best results, showing high R2 with low Root Mean Squared Error (RMSE) and low Mean Absolute Error (MAE) on both test and training datasets.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]