[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127138-en":3,"doc-seo-127138-105":29,"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":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":11,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},127138,687207022233,"Riley","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Signatures of paracrystallinity in amorphous silicon from machine-learning-driven molecular dynamics","Signatures of paracrystallinity are shown to exist within an otherwise disordered amorphous-silicon network and to remain compatible with experimental observations. Quantum-mechanically accurate, machine-learning-driven molecular-dynamics simulations systematically sample configurational space of quenched silicon, enabling clarification of the boundary between amorphization and crystallization. Structural and local-energy descriptors are used to compare paracrystalline models with experiments, providing a unified explanation for the long-standing CRN versus paracrystalline interpretations.","Article [https://doi.org/10.1038/s41467-025-57406-4](https://doi.org/10.1038/s41467-025-57406-4)  \nSignatures of paracrystallinity in amorphous silicon from machine-learning-driven molecular dynamics  \nReceived: 27 August 2024  \n\n| Accepted: 18 February 2025 |\n| --- |\n|  |\n| Check for updates |\n\nLouise A. M. Rosset 1, David A. Drabold 2 & Volker L. Deringer 1   \nThe structure of amorphous silicon has been studied for decades. The two main theories are based on a continuous random network and on a ‘paracrystalline’ model, respectively—the latter defined as showing localized structural order resembling the crystalline state whilst retaining an overall amorphous network. However, the extent of this local order has been unclear, and experimental data have led to conflicting interpretations. Here we show that signatures of paracrystallinity in an otherwise disordered network are indeed compatible with experimental observations for amorphous silicon. We use quantum-mechanically accurate, machine-learning-driven simulations to systematically sample the configurational space of quenched silicon, thereby allowing us to elucidate the boundary between amorphization and crystallization. We analyze our dataset using structural and local-energy descriptors to show that paracrystalline models are consistent with experiments in both regards. Our work provides a unified explanation for seemingly conflicting theories in one of the most widely studied amorphous networks.  \nAmorphous silicon (a-Si) is one of the most widely studied disordered network solids1–4, owing in equal parts to fundamental interest and to its range of applications. In particular, a-Si has a larger band gap than its crystalline counterpart, which is useful for solar-cell heterojunctions and thin-film transistors5,6, while its low mechanical loss makes ita candidate next-generation interferometer mirror coating material in the detection of gravitational waves using the LIGO or VIRGO instruments7,8.  \nA great challenge to understanding the ‘true’ local structure of a-Si is that there are various preparation methods, including self-ion implantation9, laser glazing10, or evaporation11, and that the structure of the resulting films depends strongly on the way by which they were made. In particular, the density9,12, coordination environments13,14, and the presence of voids15,16 vary from one sample to the next. While some authors regard self-ion-implanted a-Si as the highest quality a-Si, this must be understood to be only one example of the material, albeit superbly characterized.  \nFrom foundational work in the 1930s17,18 has emerged the currently most widely accepted model for the structure of a-Si, known as  \nthe continuous random network (CRN). The CRN model is characterized by minimal deviation from 4-fold coordination and complete absence of long-range structural order. Computations using bondswitching methods19,20 have helped to popularize the CRN model. While a-Si cannot be experimentally quenched from the melt in bulk form21, machine-learning- (ML-) based interatomic potentials22 have recently enabled molecular dynamics (MD) simulations of quenching bulk a-Si at rates of 1011 K s−1 (ref. 23) and slower24. Such rates are comparable to those used in laser quenching experiments25.  \nDespite the simplicity of the CRN model, and the fact that it isnow widely seen as the preferred way to describe a-Si1, this model is not without challenges. The main argument against the CRN model is that it fails to capture the degree ofmedium-range order seen in fluctuation electron microscopy (FEM) experiments on a-Si26. Instead, an alternative explanation consistent with FEM data has been proposed26,27, known as the ‘paracrystalline’ model. The latter is defined as a strained nanocrystal embedded in an amorphous CRN matrix, without sharp grain boundaries26. Such paracrystalline structures have recently been synthesized and experimentally and computationally characterized for  \n1Department of Chemis","cbCaifbshPtVpiKR","https://ap.wps.com/l/cbCaifbshPtVpiKR","pdf",1385771,1,"English","en",105,"# Introduction\n## Amorphous silicon and competing structural models\n## CRN model and its limitations\n## Paracrystalline model and experimental conflicts\n# Results\n## Continuous range from disorder to order\n## Machine-learning driven sampling and analysis\n## Revised paracrystalline model and coexistence of phases","[{\"question\":\"What are paracrystalline signatures in amorphous silicon, and why are they important?\",\"answer\":\"They represent localized structural order resembling crystalline features while the overall network remains amorphous. The study argues these signatures can be present without forcing a fully ordered phase, helping reconcile conflicting interpretations.\"},{\"question\":\"How does the study use machine-learning-driven molecular dynamics to address the CRN vs paracrystalline debate?\",\"answer\":\"It employs quantum-mechanically accurate, teacher–student machine-learning simulations to systematically sample the configurational space of quenched silicon. This allows exploration of intermediate structures between disorder and crystallization.\"},{\"question\":\"What methods are used to analyze the simulation dataset?\",\"answer\":\"The dataset is analyzed using structural and local-energy descriptors. These descriptors quantify how paracrystalline models match experimental constraints both structurally and energetically.\"}]","Signatures of paracrystallinity in amorphous silicon from machine-learning-driven molecular dynamics | PDF",1785937123,20,{"code":4,"msg":30,"data":31},"ok",{"site_id":23,"language":22,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":85,"head_meta":87,"extra_data":89,"updated_unix":27},"signatures-of-paracrystallinity-in-amorphous-silicon-from-machine-learning-driven-molecular-dynamics","",{"@graph":35,"@context":84},[36,53,67],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/signatures-of-paracrystallinity-in-amorphous-silicon-from-machine-learning-driven-molecular-dynamics/127138/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":22,"description":14,"dateModified":61,"datePublished":61,"encodingFormat":60,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":64,"interactionType":65,"userInteractionCount":4},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70,76,80],{"name":71,"@type":72,"acceptedAnswer":73},"What are paracrystalline signatures in amorphous silicon, and why are they important?","Question",{"text":74,"@type":75},"They represent localized structural order resembling crystalline features while the overall network remains amorphous. The study argues these signatures can be present without forcing a fully ordered phase, helping reconcile conflicting interpretations.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"How does the study use machine-learning-driven molecular dynamics to address the CRN vs paracrystalline debate?",{"text":79,"@type":75},"It employs quantum-mechanically accurate, teacher–student machine-learning simulations to systematically sample the configurational space of quenched silicon. This allows exploration of intermediate structures between disorder and crystallization.",{"name":81,"@type":72,"acceptedAnswer":82},"What methods are used to analyze the simulation dataset?",{"text":83,"@type":75},"The dataset is analyzed using structural and local-energy descriptors. These descriptors quantify how paracrystalline models match experimental constraints both structurally and energetically.","https://schema.org",{"og:url":51,"og:type":86,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":88,"canonical":51},"index,follow",{"doc_id":7,"site_id":23},{"code":4,"msg":5,"data":91},[92,96,100,104,109,114,119,122,126,129,133],{"id":20,"doc_module":4,"doc_module_name":45,"category_name":93,"show_sort_weight":94,"slug":95},"Story & Novel",90,"story-novel",{"id":46,"doc_module":4,"doc_module_name":45,"category_name":97,"show_sort_weight":98,"slug":99},"Literature",80,"literature",{"id":52,"doc_module":4,"doc_module_name":45,"category_name":101,"show_sort_weight":102,"slug":103},"Exam",70,"exam",{"id":105,"doc_module":4,"doc_module_name":45,"category_name":106,"show_sort_weight":107,"slug":108},5,"Comic",60,"comic",{"id":110,"doc_module":4,"doc_module_name":45,"category_name":111,"show_sort_weight":112,"slug":113},6,"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":45,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":45,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":45,"category_name":124,"show_sort_weight":28,"slug":125},9,"Religion & Spirituality","religion-spirituality",{"id":28,"doc_module":4,"doc_module_name":45,"category_name":127,"show_sort_weight":28,"slug":128},"World Cup","world-cup",{"id":130,"doc_module":4,"doc_module_name":45,"category_name":131,"show_sort_weight":130,"slug":132},10,"Lifestyle","lifestyle",{"id":134,"doc_module":4,"doc_module_name":45,"category_name":135,"show_sort_weight":105,"slug":136},19,"General","general"]