[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121169-en":3,"doc-seo-121169-105":30,"detail-sidebar-cat-0-en-105":90},{"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},121169,687197207639,"Asher","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Accelerating the discovery of high-mobility molecular semiconductors - a machine learning approach","Two-dimensionality of charge transport strongly influences charge carrier mobility in organic semiconductors, but evaluating it via quantum-chemical calculations is expensive for large-scale screening. A machine-learning strategy is developed to predict whether the key two-dimensional parameter lies in a desirable range without performing quantum-chemical computations. Using a large database with known 2D values, multiple models are trained with chemical and geometrical descriptors. LightGBM achieves 95% prediction accuracy, enabling systematic identification of high-mobility molecular semiconductors.","Open Access Article . Published on 31 January 2025. Downloaded on 2/ 10/2025 1:3 1:24 PM .  \nhicle is licensed under a Creative C mmons A 3 0 U d[ttr .](ttr .)ibution-NonCommercial npor e nce.  \nChemComm  \n|  |  |  |\n| --- | --- | --- |\n|  | COMMUNICATION View Article Online |  |\n| View Journal |  |  |\n\nCite this: DOI: 10. 1039/d4cc04200j  \nReceived 17th August 2024,  \nAccepted 28th January 2025 DOI: 10.1039/d4cc04200j[rsc.li/chemcomm](rsc.li/chemcomm)  \nAccelerating the discovery of high-mobility molecular semiconductors: a machine learning approach†  \nTahereh Nematiaram,  *a Zenon Lamproub and Yashar Moshfeghi b  \nThe two-dimensionality (2D) of charge transport significantly affects charge carrier mobility in organic semiconductors, making it a key target for materials discovery and design. Traditional quantumchemical methods for calculating 2D are resource-intensive, especially for large-scale screening, as they require computing charge transfer integrals for all unique pairs of interacting molecules. We explore the potential of machine learning models to predict whether this parameter will fall within a desirable range without performing any quantum-chemical calculations. Using a large database of molecular semiconductors with known 2D values, we evaluate various machinelearning models using chemical and geometrical descriptors. Our findings demonstrate that the LightGBM outperforms others, achieving 95% accuracy in predictions. These results are expected to facilitate the systematic identification of high-mobility molecular semiconductors.  \nMolecular semiconductors are promising candidates for the development of lightweight, low-cost, and flexible optoelectronic devices.1–3 These materials are increasingly utilised in various applications, such as light-emitting diodes (OLEDs),4,5 field-effect transistors (OFETs),6,7 and photovoltaic devices (OPVs) .8,9 The performance of optoelectronic devices is heavily influenced by the efficiency of charge transport processes.10–12 Despite the critical importance of enhancing device efficiency, the discovery of high-mobility molecular semiconductors has been limited, highlighting the challenges inherent in identifying such materials. Traditional experimental trial-and-error methods have only yielded a few high-mobility materials, with further efforts primarily focused on slight modifications of existing core structures.13,14 Also, the complex physics governing charge transport has restricted theoretical approaches to evaluating only a few materials.15–19  \na Department of Pure and Applied Chemistry, University of Strathclyde, 295  \nCathedral Street, Glasgow G1 1XL, UK. E-mail: [tahereh.nematiaram@strath.ac.uk](tahereh.nematiaram@strath.ac.uk)  \nb Department of Computer and Information Sciences, University of Strathclyde, 26 Richmond Street, Glasgow G1 1XH, UK  \n† Electronic supplementary information (ESI) available. See DOI: [https://doi.org/](https://doi.org/)[ ](https://doi.org/)[10.1039/d4cc04200j](10.1039/d4cc04200j)  \nRecent advances in computational frameworks and theoretical modelling have significantly improved the search for high-mobility materials. High throughput virtual screening (HTVS), a process in which large libraries of molecules are analysed using theoretical techniques and narrowed down to a small set of promising candidates for experimental verification, now enables the evaluation of vast chemical libraries.20–25 This approach enhances the probability of identifying novel high-mobility semiconductors and provides insights into the fundamental physics governing charge transport.26–29 Furthermore, a notable side benefit of HTVS is the generation of extensive databases containing computed physical properties of these molecules which facilitate the application of machine learning (ML) techniques to predict and optimise properties of new molecular systems.30,31 As an example of HTVS studies, Schober et al.29 devised a screening method to identify organic semiconductors ","cbCaieDV7xnkP7BF","https://ap.wps.com/l/cbCaieDV7xnkP7BF","pdf",475585,1,4,"English","en",105,"# Introduction\n## Motivation: 2D charge transport and mobility\n## Traditional approaches and their limitations\n# Machine learning approach\n## Dataset and descriptors\n## Model evaluation\n# Results and implications\n## LightGBM performance\n## Enabling systematic discovery","[{\"question\":\"Why is two-dimensionality (2D) important for organic semiconductor mobility?\",\"answer\":\"The two-dimensionality of charge transport significantly affects charge carrier mobility, making it a key target for materials discovery and design.\"},{\"question\":\"How does the proposed method avoid quantum-chemical calculations?\",\"answer\":\"Machine learning models predict whether the 2D parameter is within a desirable range using chemical and geometrical descriptors learned from a database of known 2D values.\"},{\"question\":\"Which machine learning model performed best, and what accuracy was achieved?\",\"answer\":\"LightGBM outperforms other models, achieving 95% accuracy in predictions.\"}]","Accelerating the discovery of high-mobility molecular semiconductors - a machine learning approach | PDF",1785734184,10,{"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":85,"head_meta":87,"extra_data":89,"updated_unix":28},"accelerating-the-discovery-of-high-mobility-molecular-semiconductors-a-machine-learning-approach","",{"@graph":36,"@context":84},[37,53,67],{"@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":21},"https://docshare.wps.com/document/accelerating-the-discovery-of-high-mobility-molecular-semiconductors-a-machine-learning-approach/121169/",{"url":52,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":61,"encodingFormat":60,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":41,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":64,"interactionType":65,"userInteractionCount":20},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70,76,80],{"name":71,"@type":72,"acceptedAnswer":73},"Why is two-dimensionality (2D) important for organic semiconductor mobility?","Question",{"text":74,"@type":75},"The two-dimensionality of charge transport significantly affects charge carrier mobility, making it a key target for materials discovery and design.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"How does the proposed method avoid quantum-chemical calculations?",{"text":79,"@type":75},"Machine learning models predict whether the 2D parameter is within a desirable range using chemical and geometrical descriptors learned from a database of known 2D values.",{"name":81,"@type":72,"acceptedAnswer":82},"Which machine learning model performed best, and what accuracy was achieved?",{"text":83,"@type":75},"LightGBM outperforms other models, achieving 95% accuracy in predictions.","https://schema.org",{"og:url":52,"og:type":86,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":88,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":91},[92,96,100,104,109,114,119,122,127,130,133],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":93,"show_sort_weight":94,"slug":95},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":97,"show_sort_weight":98,"slug":99},"Literature",80,"literature",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":101,"show_sort_weight":102,"slug":103},"Exam",70,"exam",{"id":105,"doc_module":4,"doc_module_name":46,"category_name":106,"show_sort_weight":107,"slug":108},5,"Comic",60,"comic",{"id":110,"doc_module":4,"doc_module_name":46,"category_name":111,"show_sort_weight":112,"slug":113},6,"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":29,"doc_module":4,"doc_module_name":46,"category_name":131,"show_sort_weight":29,"slug":132},"Lifestyle","lifestyle",{"id":134,"doc_module":4,"doc_module_name":46,"category_name":135,"show_sort_weight":105,"slug":136},19,"General","general"]