[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118970-en":3,"doc-seo-118970-105":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},118970,4810365810221,"Aurora","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Phase Transition in Silicon from Machine Learning Informed Metadynamics - Abstract","Investigating reconstructive phase transitions in large systems demands a computational framework whose cost scales with system size, making conventional density functional theory (DFT) prohibitively expensive for long-time simulations. This study combines well-trained machine learning potentials with metadynamics, using a deep potential model trained on extensive ab initio datasets at 25 GPa, to accelerate phase-transition exploration. The method captures transition pathways and the emergence of polycrystalline silicon, including grain development and dislocation defects, under high-pressure stress conditions.","􀁸  \nResearch Article  \nPhase Transition in Silicon from Machine Learning Informed Metadynamics  \nMangladeep Bhullar, Zihao Bai, Akinwumi Akinpelu, Prof. Dr. Yansun Yao First published: 22 April 2024  \n[https://doi.org/10.1002/cphc.202400090](https://doi.org/10.1002/cphc.202400090)  \nRead the full text  \nPDF  \nTOOLS  \nSHARE  \nGraphical Abstract  \nThe article reveals the phase transition in a system of 4096 Silicon atoms from cubic diamond to a concluding amalgam of FCC and HCP phases with the use of machine learning potentials (MLP) built from the deep neural network (DNN) mechanism. The Deep Potential is constructed by training on extensive ab initio datasets depicting the behavior of the system under 25 GPa.  \nAbstract  \nInvestigating reconstructive phase transitions in large-sized systems requires a highly efficient computational framework with computational cost proportional to the system size. Traditionally, widely used frameworks such as density functional theory (DFT) have been prohibitively expensive for extensive simulations on large systems that require long-time scales. To address this challenge, this study employed well-trained machine learning potential to simulate phase transitions in a largesize system. This work integrates the metadynamics simulation approach with machine learning potential, specifically deep potential, to enhance computational efficiency and accelerate the study of phase transition and consequent development of grains and dislocation defects in a system. The new method is demonstrated using the phase transitions of bulk silicon under high pressure. This approach has revealed the transition path and formation of polycrystalline silicon systems under specific stress conditions, demonstrating the effectiveness of deep potential-driven metadynamics simulations in gaining insights into complex material behaviors in large-sized systems.  \nConflict of interests  \nThe authors declare no conflict of interest.  \nOpen Research  \nReferences  \nEarly View  \nOnline Version of Record before inclusion in an issue  \ne202400090  \n􀁸  \nReferences  \n􀁸  \nRelated  \n􀁸 Information  \nRecommended  \n􀁸  Giant Deuteron Migration During the Isosymmetric Phase Transition in Deuterated 3,5-Pyridinedicarboxylic Acid  \nSamantha J. Ford, Oliver J. Delamore, John S. O. Evans , Garry J. McIntyre, Mark R. Johnson , Ivana Radosavljević Evans  \nChemistry – A European Journal  \n􀁸  Target Designing Phase Transition Materials through Halogen Substitution  \nHao Cheng, Meng-Juan Yang , Yu-Qiu Xu, Meng-Zhen Li , Yong Ai  \nChemPhysChem  \n􀁸  Ionic and Optical Properties of Methylammonium Lead Iodide Perovskite across the Tetragonal–Cubic Structural Phase Transition  \nMd Nadim Ferdous Hoque, Nazifah Islam , Zhen Li , Guofeng Ren, Kai Zhu, Zhaoyang Fan  \nChemSusChem  \n􀁸  Metadynamics  \nAlessandro Barducci, Massimiliano Bonomi, Michele Parrinello  \nWIREs Computational Molecular Science  \n􀁸  Machine Learning Molecular Dynamics Shows Anomalous Entropic Effect on Catalysis through Surface Pre-melting of Nanoclusters  \nFu-Qiang Gong, Yun-Pei Liu, Ye Wang, Weinan E , Zhong-Qun Tian, Jun Cheng  \nAngewandte Chemie International Edition  \nDownload PDF  \n Back  \n􀁸  \nCONNECT WITH WILEY  \n􀁸  The Wiley Network  \n􀁸 Wiley Press Room  \nCopyright © 1999-2024 John Wiley & Sons, Inc or related companies. All rights reserved, including rights for text and data mining and training of artificial technologies or similar technologies.","cbCaioQzKTlqGvb6","https://ap.wps.com/l/cbCaioQzKTlqGvb6","pdf",292420,1,5,"English","en",105,"# Graphical Abstract\n## Method Overview\n## Findings and Demonstrated Effectiveness\n## Declarations","[{\"question\":\"Why is density functional theory (DFT) challenging for reconstructive phase transitions in large systems?\",\"answer\":\"DFT is prohibitively expensive when simulations require long times and large system sizes, since the computational cost scales with system size.\"},{\"question\":\"What modeling approach does the study use to improve efficiency?\",\"answer\":\"It integrates metadynamics with a machine learning potential, specifically a Deep Potential trained on large ab initio datasets representing system behavior at 25 GPa.\"},{\"question\":\"What does the approach reveal about silicon under high pressure?\",\"answer\":\"It reveals the transition path and formation of polycrystalline silicon, including grain development and dislocation defects under specific stress conditions.\"}]","Phase Transition in Silicon from Machine Learning Informed Metadynamics - Abstract | PDF",1785721272,13,{"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},"phase-transition-in-silicon-from-machine-learning-informed-metadynamics-abstract","",{"@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/phase-transition-in-silicon-from-machine-learning-informed-metadynamics-abstract/118970/",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","2026-08-04","2026-08-03",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 is density functional theory (DFT) challenging for reconstructive phase transitions in large systems?","Question",{"text":76,"@type":77},"DFT is prohibitively expensive when simulations require long times and large system sizes, since the computational cost scales with system size.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"What modeling approach does the study use to improve efficiency?",{"text":81,"@type":77},"It integrates metadynamics with a machine learning potential, specifically a Deep Potential trained on large ab initio datasets representing system behavior at 25 GPa.",{"name":83,"@type":74,"acceptedAnswer":84},"What does the approach reveal about silicon under high pressure?",{"text":85,"@type":77},"It reveals the transition path and formation of polycrystalline silicon, including grain development and dislocation defects under specific stress conditions.","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},[94,98,102,106,110,115,120,123,128,131,135],{"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":21,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},"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":21,"slug":138},19,"General","general"]