[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123613-en":3,"doc-seo-123613-105":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},123613,687197100911,"Himbo","https://ap-avatar.wpscdn.com/avatar/a000239b6f1da00475?x-image-process=image/resize,m_fixed,w_180,h_180&k=1785132997149421697",8,"Research & Report","Machine learning for accelerated bandgap prediction in strain-engineered quaternary III-V semiconductors - Research focused abstract","Quaternary III-V semiconductors are highlighted as key candidates for optoelectronic technologies where the bandgap size and whether it is direct or indirect largely determine device performance. The document proposes a computational pipeline combining first-principles calculations with machine learning to learn bandgap magnitudes and their nature from composition and strain features, using density functional theory accuracy. The resulting model is computationally efficient and highly accurate, enabling rapid prediction across many compositions and strain values for virtual screening of multinary III-V materials in optoelectronic applications.","arXiv :2305 .03666v1 [ cond-mat .mtrl-sci ] 5 May 2023  \nMachine learning for accelerated bandgap prediction in strain-engineered quaternary III-V semiconductors†  \nBadal Mondal,ab Julia Westermayr,ac and Ralf Tonner-Zech􀀃a  \nQuaternary III-V semiconductors are one of the major promising material classes in optoelectronics . The bandgap and its character, direct or indirect, are the most important fundamental properties determining the performance and characteristics of optoelectronic devices. Experimental approaches screening a large range of possible combinations of III-and V-elements with variations in composition and strain are impractical for every target application. We present a combination of accurate firstprinciples calculations and machine learning based approaches to predict the properties of the bandgap for quaternary III-V semiconductors . By learning bandgap magnitudes and their nature at density functional theory accuracy based solely on the composition and strain features of the materials as an input, we develop a computationally efficient yet highly accurate machine learning approach that can be applied to a large number of compositions and strain values. This allows for a computationally efficient prediction of a vast range of materials under different strains, offering the possibility for virtual screening of multinary III-V materials for optoelectronic applications.  \n1 Introduction  \nSemiconductor compounds are central to modern optoelectronics and find applications in various fields, such as solar cells, light-emitting diodes, optical telecommunication, and photovoltaics. 1–11 One of the fundamental properties determining the performance of such optoelectronic devices is the bandgap. The tuning of the size and type of bandgaps is one of the major goals in the field of optoelectronics. Varying the relative composition in compound semiconductors is one of the major approaches here. 12–25 Alternatively, straining the system can be used to modify the bandgaps.26–37 By combining these two approaches, bandgaps can be tailored over a wide range of values, enabling the enormous diversity in device applications. 12–25,38–42 Due to a vast composition space, quaternary III-V semiconductors offer a unique opportunity in materials design.43 However, identifying tailored materials for each target application requires assessing the dependence of the bandgap on composition and strain for a large set of materials. Because of the tremendous effort necessary for the synthesis of unknown materials, experimental approaches of screening the vast chemical space of all possible combinations of III-(or group 13) and V-(or group 15) el-  \na Wilhelm-Ostwald-Institut für Physikalische und Theoretische Chemie, Universität Leipzig, 04103 Leipzig, Germany. E-mail: [ralf.tonner@uni-leipzig.de](ralf.tonner@uni-leipzig.de)[b](b Fachbereich Physik)[ Fachbereich Physik](b Fachbereich Physik), [Philipps](Philipps)-Universität Marburg, 35032 Marburg, Germany. c Center for Scalable Data Analytics and Artificial Intelligence, Dresden/Leipzig, Ger many.  \n† Electronic Supplementary Information (ESI) available. See DOI:  \n00.0000/00000000 .  \nements with variation in composition and strain, thus, are not practical. 12,16,19,22,24,25 Therefore, theoretical models that are both accurate and computationally efficient are often the only viable choice for high-throughput virtual screening for materials design. 44,45  \nIn recent years, density functional theory (DFT) methods based on computationally efficient density functionals like those based on the local density approximation (LDA) 46 or generalized gradient approximation (GGA) 47 have proven to be powerful and successful tools for such high-throughput material screening. However, large errors for semiconductor bandgaps, which can bein the range of 50% of the bandgap value, 48–50 are common. Better accuracy can be achieved with methods such as hybrid functionals, 51,52 many-body perturbation theory (GW), 53–55 ","cbCaiqMZcg3MSlw8","https://ap.wps.com/l/cbCaiqMZcg3MSlw8","pdf",8820973,1,19,"English","en",105,"# Introduction\n## Bandgap as a key property in optoelectronics\n## Challenges of experimental screening and need for efficient theory\n## Accuracy limits of DFT and alternatives\n## Machine learning for bandgap prediction\n## Goal: strained quaternary III-V bandgap model","[{\"question\":\"Why is the bandgap crucial for optoelectronic device performance?\",\"answer\":\"The bandgap size and its character (direct vs. indirect) are fundamental properties that determine how optoelectronic devices behave.\"},{\"question\":\"What limitation motivates the proposed machine learning approach?\",\"answer\":\"Experimental screening of the large composition and strain space is impractical, while common high-accuracy theoretical methods remain too costly for high-throughput studies.\"},{\"question\":\"How does the method accelerate bandgap prediction?\",\"answer\":\"It trains a machine learning model on first-principles (DFT-level) bandgap data using only composition and strain as inputs, yielding an efficient surrogate for predicting bandgaps over many compositions and strain values.\"}]","Machine learning for accelerated bandgap prediction in strain-engineered quaternary III-V semiconductors - Research focused abstract | PDF",1785817632,48,{"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-for-accelerated-bandgap-prediction-in-strain-engineered-quaternary-iii-v-semiconductors-research-focused-abstract","",{"@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-for-accelerated-bandgap-prediction-in-strain-engineered-quaternary-iii-v-semiconductors-research-focused-abstract/123613/",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 is the bandgap crucial for optoelectronic device performance?","Question",{"text":75,"@type":76},"The bandgap size and its character (direct vs. indirect) are fundamental properties that determine how optoelectronic devices behave.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What limitation motivates the proposed machine learning approach?",{"text":80,"@type":76},"Experimental screening of the large composition and strain space is impractical, while common high-accuracy theoretical methods remain too costly for high-throughput studies.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the method accelerate bandgap prediction?",{"text":84,"@type":76},"It trains a machine learning model on first-principles (DFT-level) bandgap data using only composition and strain as inputs, yielding an efficient surrogate for predicting bandgaps over many compositions and strain values.","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":21,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},"General","general"]