[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122864-en":3,"doc-seo-122864-105":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},122864,4398048949847,"Eliana","https://ap-avatar.wpscdn.com/avatar/400002536579ef2da7f?_k=1778318612642679267",8,"Research & Report","Learning from machine learning - the case of band-gap directness in semiconductors","Semiconductors with direct versus indirect band gaps determine suitability for different optoelectronic applications, yet no unified explanation exists for why a material exhibits one or the other. This work applies the interpretable VAX (multiVariate dAta eXplanation) method to a Materials Project dataset with 10,000+ entries, using electronic and atomic features to train random forest models. Results show symmetry as a key determinant, with d-band presence and relative atomic orbital energies further shaping directness or indirectness, including analysis across zincblende, rocksalt, wurtzite, and perovskite subgroups.","Learning from machine learning  \nCitation for published version (APA):  \nOgoshi, E. , Popolin-Neto, M. , Acosta, C. M. , Nascimento, G. M. , Rodrigues, J. N. B. , Oliveira, O. N. , Paulovich, F. V. , & Dalpian, G. M. (2024) . Learning from machine learning: the case of band-gap directness in semiconductors. Discover Materials, 4(1), Article 6. [https://doi.org/10.1007/s43939-024-00073-x](https://doi.org/10.1007/s43939-024-00073-x)  \nDocument license:  \nCC BY  \nDOI:  \n10.1007/s43939-024-00073-x  \nDocument status and date:  \nPublished: 01/12/2024  \nDocument Version:  \nPublisher’s PDF, also known as Version of Record (includes final page, issue and volume numbers)  \nPlease check the document version of this publication:  \n• A submitted manuscript is the version of the article upon submission and before peer-review. There can be important differences between the submitted version and the official published version of record. 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Aug. 2026  \nResearch  \nLearning from machine learning: the case of band‑gap directness in semiconductors  \nElton Ogoshi1 · Mário Popolin‑Neto2,3 · Carlos Mera Acosta1 · Gabriel M. Nascimento1 · João N. B. Rodrigues1,4 · Osvaldo N. Oliveira Jr5 · Fernando V. Paulovich6 · Gustavo M. Dalpian1,7  \nReceived: 30 October 2023 / Accepted: 8 February 2024  \n© The Author(s) 2024 OPEN  \nAbstract  \nHaving a direct or indirect band gap can influence the potential applications of a semiconductor, for indirect band gap materials are usually not suitable for optoelectronic devices. Even though this is a fundamental property of semiconducting materials, discussed in textbooks, no unified theory exists to explain why a material has a direct or indirect band gap. Here we used an interpretable machine learning model, the multiVariate dAta eXplanation (VAX) method, to gather information from a dataset of materials extracted from the Materials Project. The dataset contains more than 10000 entries, and atomic properties such as the number of electrons, electronic affinity and orbital energies were used as features to build random forest models that successfully explain the directness of the band gaps. Our results indicate that symmetry is an important feature that dictates the target property, which is the reason why our analysis is made based on sub-groups with similar structures. These sub-groups include materials with zincblende, rocksalt, wurtzite, and perovskite structures. Besides the symmetry of the materials, the existence or not of d bands and the relative energy of atomic orbitals were","cbCaiaP4K3tnllXk","https://ap.wps.com/l/cbCaiaP4K3tnllXk","pdf",4181220,1,15,"English","en",105,"# Abstract\n# Introduction\n## Machine learning in materials research\n## Dataset and modeling approach\n## Interpretable analysis with VAX","[{\"question\":\"Why does band-gap directness matter for semiconductors?\",\"answer\":\"Direct and indirect band gaps influence which optoelectronic applications a semiconductor can support. Indirect band-gap materials are generally less suitable for optoelectronic devices.\"},{\"question\":\"What interpretable machine learning method is used in the study?\",\"answer\":\"The study uses the VAX method (multiVariate dAta eXplanation) to extract physical interpretation from a materials dataset.\"},{\"question\":\"Which factors were found to explain whether a band gap is direct or indirect?\",\"answer\":\"Symmetry is identified as an important driver, while the existence of d bands and the relative energy of atomic orbitals also significantly affect directness versus indirectness.\"}]","Learning from machine learning - the case of band-gap directness in semiconductors | PDF",1785813413,38,{"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},"learning-from-machine-learning-the-case-of-band-gap-directness-in-semiconductors","",{"@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/learning-from-machine-learning-the-case-of-band-gap-directness-in-semiconductors/122864/",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},"Why does band-gap directness matter for semiconductors?","Question",{"text":76,"@type":77},"Direct and indirect band gaps influence which optoelectronic applications a semiconductor can support. Indirect band-gap materials are generally less suitable for optoelectronic devices.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"What interpretable machine learning method is used in the study?",{"text":81,"@type":77},"The study uses the VAX method (multiVariate dAta eXplanation) to extract physical interpretation from a materials dataset.",{"name":83,"@type":74,"acceptedAnswer":84},"Which factors were found to explain whether a band gap is direct or indirect?",{"text":85,"@type":77},"Symmetry is identified as an important driver, while the existence of d bands and the relative energy of atomic orbitals also significantly affect directness versus indirectness.","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"]