[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127063-en":3,"doc-seo-127063-105":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},127063,962084931830,"Theodore","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Understanding mechanical dissipation in glasses through machine learning molecular dynamics","A focused study develops machine-learning molecular dynamics methods to investigate mechanical dissipation in vitreous silica-based glasses relevant to gravitational-wave detector mirrors. The work frames mechanical loss as the origin of thermal noise via the fluctuation–dissipation theorem and targets materials based on alternating SiO2 and Ta2O5 layers doped with titanium. It combines equivariant neural-network interatomic potentials, training and optimization procedures, and subsequent calculations of structural and elastic properties to extract the mechanical dissipation factor Q−1.","UNIVERSIT `A DEGLI STUDI DI PADOVA Dipartimento di Fisica e Astronomia “Galileo Galilei”  \nMaster degree in Physics of Data  \nFinal dissertation  \nUnderstanding mechanical dissipation in glasses through machine learning molecular dynamics  \nThesis supervisor  \nProf. Paolo Umari  \nCandidate  \nAlessio Saccomani  \nAcademic year 2023/2024  \nContents  \n1 Introduction 2  \n2 Equivariant Neural Networks 4  \n2.1 Message-Passing Interatomic Potentials .......................... 4  \n2.2 Nequip ............................................. 6  \n2.3 Allegro ............................................. 9  \n2.4 Setup ............................................. 12  \n2.4.1 Core Model Structure ................................ 12  \n2.4.2 Cutoff Radius and Neighbor Estimation ...................... 13  \n2.4.3 Radial Basis and Symmetry ............................. 13  \n2.4.4 Allegro Layers and Multi-body Interaction Capture ................ 13  \n2.4.5 Dataset Handling and Chemical Species Mapping ................. 13  \n2.4.6 Logging and Monitoring ............................... 14  \n2.4.7 Training Parameters ................................. 14  \n2.4.8 Loss Function and Optimization .......................... 14  \n2.5 Differences and overview ................................... 15  \n2.6 Machine Learning Potentials in LAMMPS ......................... 16  \n3 Ta2 O5 and its Application 17  \n4 Calculation of physical properties 20  \n4.1 DFT .............................................. 20  \n4.2 RDF .............................................. 23  \n4.3 Structure Factor S (q) .................................... 25  \n4.4 ADF .............................................. 26  \n4.5 Dynamical Matrix and VDOS ................................ 27  \n4.6 Elastic Properties and Mechanical Dissipation Factor Q−1 ................ 28  \n4.7 Computation in Lammps .................................. 29  \n5 Allegro Analysis 31  \n5.1 Computational Time ..................................... 34  \n5.2 Training Results ....................................... 39  \n6 Molecular Dynamics Analysis 45  \n6.1 Comparison with DFT .................................... 49  \n6.2 Results with different cut-off ................................ 52  \n6.3 Elastic Properties ....................................... 57  \n7 Conclusions 59  \nChapter 1  \nIntroduction  \nMolecular Dynamics (MD) simulations lie at the core of computational physics, chemistry and biology, playing a critical role in advancing research across these fields. By complementing experimental techniques, MD simulations utilize the computational power of modern computers to model and predict the behavior of complex systems at the atomic and molecular levels. Since their inception in the 1950s, MD simulations have evolved dramatically, becoming essential tools in fields such as physics, biology, chemistry and materials science. Today, MD simulations allow researchers to tackle problems that would be challenging, if not impossible, to explore experimentally.  \nThe central idea behind MD is to simulate the time evolution of a system of particles such as atoms, molecules, or larger biomolecular structures based on their interactions [1–3] . These interactions are typically governed by classical mechanics, primarily by Newton’s equations of motion. By solving these equations for each particle in the system, MD simulations provide a time-resolved view of how systems behave, evolve, and respond to various conditions.  \nOne of the main challenges in MD is balancing accuracy with computational efficiency, especially for large systems. To achieve this balance, researchers employ a range of methods, including empirical force fields and more computationally demanding quantum mechanical approaches. Empirical force fields approximate interactions using parameterized potentials, which allow for the simulation of large systems over longer timescales but may lack the precision required to capture detailed electronic effects. First-princi","cbCaiaPEjlxJeRGK","https://ap.wps.com/l/cbCaiaPEjlxJeRGK","pdf",6012863,1,64,"English","en",105,"# Introduction\n## Equivariant Neural Networks\n## Ta2 O5 and its Application\n## Calculation of physical properties\n## Allegro Analysis\n## Molecular Dynamics Analysis\n## Conclusions","[{\"question\":\"Why is mechanical dissipation important for gravitational-wave detectors?\",\"answer\":\"Mechanical dissipation generates thermal noise in detector mirrors. This noise limits detector sensitivity and is linked to material internal dissipation through the fluctuation–dissipation theorem.\"},{\"question\":\"What modeling approaches are compared in the study?\",\"answer\":\"The work contrasts first-principles calculations such as DFT with empirical or machine-learning-based molecular dynamics approaches. It aims to balance DFT accuracy with better computational efficiency.\"},{\"question\":\"How is the mechanical dissipation factor Q−1 obtained?\",\"answer\":\"Elastic properties and dynamical-mechanical quantities are computed from MD-related analyses and lattice-related descriptors, culminating in the extraction of the mechanical dissipation factor Q−1.\"}]","Understanding mechanical dissipation in glasses through machine learning molecular dynamics | PDF",1785936616,161,{"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},"understanding-mechanical-dissipation-in-glasses-through-machine-learning-molecular-dynamics","",{"@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/understanding-mechanical-dissipation-in-glasses-through-machine-learning-molecular-dynamics/127063/",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","2026-08-05",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},"Why is mechanical dissipation important for gravitational-wave detectors?","Question",{"text":75,"@type":76},"Mechanical dissipation generates thermal noise in detector mirrors. This noise limits detector sensitivity and is linked to material internal dissipation through the fluctuation–dissipation theorem.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What modeling approaches are compared in the study?",{"text":80,"@type":76},"The work contrasts first-principles calculations such as DFT with empirical or machine-learning-based molecular dynamics approaches. It aims to balance DFT accuracy with better computational efficiency.",{"name":82,"@type":73,"acceptedAnswer":83},"How is the mechanical dissipation factor Q−1 obtained?",{"text":84,"@type":76},"Elastic properties and dynamical-mechanical quantities are computed from MD-related analyses and lattice-related descriptors, culminating in the extraction of the mechanical dissipation factor Q−1.","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"]