[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123065-en":3,"doc-seo-123065-105":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},123065,8796095461610,"Oliver","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Machine-learning structural reconstructions for accelerated point defect calculations","Defects determine the properties of many functional materials, but identifying the most stable defect geometries is computationally difficult, especially for high-throughput studies and complex defect landscapes in alloys and disordered solids. This work addresses the bottleneck with a machine-learning surrogate model that learns defect motifs across related metal chalcogenide and mixed anion crystal families. It predicts favourable defect reconstructions for 90% of unseen cases, reducing first-principles calculations by 73%. Using CdSexTe1−x alloys as an example, the model is trained on end-member compositions and applied to obtain stable geometries of all inequivalent vacancies across varied mixing concentrations, enabling faster, more accurate defect studies for configurationally complex systems.","arXiv :2401 . 12127v1 [ cond-mat .mtrl-sci ] 22 Jan 2024  \nMachine-learning structural reconstructions for accelerated point defect calculations  \nIrea Mosquera-Lois, 1 Se´an R. Kavanagh, 1 Alex M. Ganose,2 and Aron Walsh 1, 3, a)  \n1) Thomas Young Centre & Department of Materials, Imperial College London, London SW7 2AZ, UK  \n2) Thomas Young Centre & Department of Chemistry, Imperial College London, London W12 0BZ, UK  \n3) Department of Physics, Ewha Womans University, Seoul 03760, Korea  \n(Dated: 23 January 2024)  \nDefects dictate the properties of many functional materials. To understand the behaviour of defects and their impact on physical properties, it is necessary to identify the most stable defect geometries. However, global structure searching is computationally challenging for high-throughput defect studies or materials with complex defect landscapes, like alloys or disordered solids. Here, we tackle this limitation by harnessing a machine-learning surrogate model to qualitatively explore the defect structural landscape. By learning defect motifs in a family of related metal chalcogenide and mixed anion crystals, the model successfully predicts favourable reconstructions for unseen defects in unseen compositions for 90% of cases, thereby reducing the number of first-principles calculations by 73% . Using CdSex Te 1−x alloys as an exemplar, we train a model on the end member compositions and apply it to find the stable geometries of all inequivalent vacancies for a range of mixing concentrations, thus enabling more accurate and faster defect studies for configurationally complex systems.  \na)Electronic mail: [a.walsh@imperial.ac.uk](a.walsh@imperial.ac.uk)  \n2  \nI. INTRODUCTION  \nDefects control the properties of many functional materials and devices 1 , like solar cells2 ,3 , batteries4 ,5 , catalysts6–8, and quantum computers9–12 . To discover better materials for these applications it is thus necessary to predict how their defects behave. However, defect calculations are computationally demanding. The large supercells and high level of theory required to obtain robust predictions typically limit point defect analysis to in-depth studies of specific materials. In a move towards data-driven defect workflows 13 , defect databases 14–20 and surrogate models have been developed to predict defect properties, like the dominant defect type 18 , formation 19 ,21–35 and migration35 energies, and charge transition levels 19 ,25 ,36 . By learning the relationship between defect structure and properties, these models enable high-throughput studies that quickly evaluate and screen a group of materials based on their defect behaviour.27 ,28 ,30 ,37  \nDespite progress in accelerating defect predictions, most high-throughput studies are limited in scope. Typically, their training datasets are generated assuming the ideal defect structure inherited from the crystal host, which often lies within a local minimum, thereby trapping a gradient-based optimisation algorithm in a metastable arrangement38–41 . By yielding incorrect geometries, the predicted defect properties, such as equilibrium concentrations39 ,41 ,42 , charge transition levels39 ,41 ,42 and recombination rates39 , are rendered inaccurate.8 ,43–46 . However, defect structure searching is often too expensive for highthroughput studies that target thousands of defects30 or materials with complex (defect) energy landscapes, like alloys, disordered solids, and low-symmetry crystals.  \nIn this study, we aim to reduce the computational burden of defect structure searching by introducing a machine-learning surrogate model. We build a dataset containing a set of point defect structures, energies, forces and stresses from first-principles, and use it to fine-tune a universal machine-learning force field (MLFF) and qualitatively explore the energy landscape across 132 defects. Defect reconstructions often follow common motifs41 , especially when comparing similar defects in families","cbCaidmzo80OSBA4","https://ap.wps.com/l/cbCaidmzo80OSBA4","pdf",9424611,1,65,"English","en",105,"# Introduction\n## Motivation: defect structure searching as a bottleneck\n## Data-driven defect workflows and surrogate models\n## Proposed approach: machine-learning force field and qualitative landscape exploration\n# Results\n## Learning reconstruction motifs and dimerisation focus","[{\"question\":\"为什么点缺陷的结构搜索在高通量研究中很难？\",\"answer\":\"因为点缺陷计算需要很大的超胞和高水平理论，成本高，且复杂缺陷能量景观（如合金、无序固体、低对称体系）会进一步增加搜索难度。\"},{\"question\":\"文中使用的机器学习代理模型做了什么？\",\"answer\":\"通过学习相关金属硫属化合物与混合阴离子晶体中缺陷的重构“模体”，代理模型能对未见缺陷与未见成分的候选结构进行定性探索与筛选，从而找到更可能的低能构型。\"},{\"question\":\"以 CdSexTe1−x 合金为例，模型如何帮助进行缺陷研究？\",\"answer\":\"在端元成分上训练模型后，将其用于确定不同混合浓度下所有不等价空位的稳定几何结构，从而在更快的同时提升缺陷研究的准确性。\"}]","Machine-learning structural reconstructions for accelerated point defect calculations | PDF",1785814478,164,{"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-structural-reconstructions-for-accelerated-point-defect-calculations","",{"@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-structural-reconstructions-for-accelerated-point-defect-calculations/123065/",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},"为什么点缺陷的结构搜索在高通量研究中很难？","Question",{"text":75,"@type":76},"因为点缺陷计算需要很大的超胞和高水平理论，成本高，且复杂缺陷能量景观（如合金、无序固体、低对称体系）会进一步增加搜索难度。","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"文中使用的机器学习代理模型做了什么？",{"text":80,"@type":76},"通过学习相关金属硫属化合物与混合阴离子晶体中缺陷的重构“模体”，代理模型能对未见缺陷与未见成分的候选结构进行定性探索与筛选，从而找到更可能的低能构型。",{"name":82,"@type":73,"acceptedAnswer":83},"以 CdSexTe1−x 合金为例，模型如何帮助进行缺陷研究？",{"text":84,"@type":76},"在端元成分上训练模型后，将其用于确定不同混合浓度下所有不等价空位的稳定几何结构，从而在更快的同时提升缺陷研究的准确性。","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"]