[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124673-en":3,"doc-seo-124673-105":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":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},124673,962075006959,"Anda","https://ap-avatar.wpscdn.com/avatar/e0002397efbe92a78e?_k=1776741047341049297",8,"Research & Report","Prediction of Diblock Copolymer Morphology via Machine Learning","A machine learning framework accelerates computation of diblock copolymer block polymer morphology evolution across large spatial domains and long time horizons. The method leverages timescale separation between rapid coarse-grained particle dynamics and slower mesoscopic morphological changes. Instead of empirical continuum descriptions, it learns defect annihilation mechanisms from particle-based simulations using a UNet that handles periodic and fixed boundaries of arbitrary geometry. Loss-function physics and symmetry-aware data augmentation improve fidelity. Validations cover three use cases, and explainable AI visualizations reveal morphology evolution over time. The approach enables large systems and long trajectories to study defect densities under confinement. It also highlights late-stage morphologies for modeling diffusion of particles inside a single block, with implications for directed self-assembly and materials design in microelectronics, batteries, and membranes.","arXiv :2308 . 16886v1 [physics .chem-ph] 31 Aug 2023  \nPrediction of Diblock Copolymer Morphology via  \nMachine Learning  \nHyun Park,†,‡,¶ ,\\# Boyuan Yu,§ ,\\# Juhae Park,§ Ge Sun,§ Emad Tajkhorshid, ∥ ,‡,⊥  \nJuan [J. de](J. de) Pablo,∗,§ and Ludwig Schneider ∗,§  \n†Theoretical and Computational Biophysics Group, NIH Resource Center for Macromolecular Modeling and Bioinformatics, Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA ‡Center for Biophysics and Quantitative Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA  \n¶Data Science and Learning Division, Argonne National Laboratory, Lemont, Illinois 60439,  \nUSA  \n§Pritzker School of Molecular Engineering, University of Chicago, 5640 Ellis Ave, Chicago,  \nIllinois 60637, USA  \n∥ Theoretical and Computational Biophysics Group, Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA ⊥Department of Biochemistry, University of Illinois at Urbana-Champaign, Urbana, Illinois  \n61801, USA  \n\\#Contributed equally to this work  \nE-mail: [depablo@uchicago.edu](depablo@uchicago.edu) ; [ludwigschneider@uchicago.edu](ludwigschneider@uchicago.edu)  \nAbstract  \nA machine learning approach is presented to accelerate the computation of block polymer morphology evolution for large domains over long timescales. The strategy  \nexploits the separation of characteristic times between coarse-grained particle evolution on the monomer scale and slow morphological evolution over mesoscopic scales. In contrast to empirical continuum models, the proposed approach learns stochastically driven defect annihilation processes directly from particle-based simulations. A UNet architecture that respects different boundary conditions is adopted, thereby allowing periodic and fixed substrate boundary conditions of arbitrary shape. Physical concepts are also introduced via the loss function and symmetries are incorporated via data augmentation. The model is validated using three different use cases. Explainable artificial intelligence methods are applied to visualize the morphology evolution overtime. This approach enables the generation of large system sizes and long trajectories to investigate defect densities and their evolution under different types of confinement.  \nAs an application, we demonstrate the importance of accessing late-stage morphologies for understanding particle diffusion inside a single block. This work has implications for directed self-assembly and materials design in micro-electronics, battery materials, and membranes.  \nIntroduction  \nDiblock copolymers have the ability to form nano-structured materials through microphase separation. Their equilibrium bulk properties 1 are well understood, but questions remain about their dynamics and morphological evolution over long time scales. Further research into their dynamics is necessary to develop emerging applications like battery materials 2–5 and micro-electronic device fabrication. This issue is particularly critical in the context of the kinetics of directed self-assembly (DSA)6–17 . DSA, where copolymers are of interest for pattern rectification in nano-lithography 18 19 . Note that with the advent of extreme ultraviolet lithography (EUV) for micro-electronics fabrication, the removal of defects in target lamellar structures has become increasingly important 20–22 .  \nFor battery materials, micro-electronic and other applications, it is important to under-  \nstand not only the evolution of the bulk morphology of diblock copolymers, but also their interaction with boundaries. Boundaries can affect the final equilibrium morphology 23,24 of these materials. For example, symmetric diblock copolymers that form lamellae will generate ”standing” lamellae on a neutral surface, but will form ”parallel” lamellae on an attractive surface. More complex geometries can be conceiv","cbCainbWoTMASusV","https://ap.wps.com/l/cbCainbWoTMASusV","pdf",45226601,1,51,"English","en",105,"# Abstract\n# Introduction\n## Motivation: long-time morphology dynamics\n## Applications: micro-electronics and battery materials\n## Role of confinement and boundaries\n## Existing approaches: particle simulations and continuum models\n## Machine learning integration and problem setup","[{\"question\":\"What problem does the machine learning approach aim to solve?\",\"answer\":\"It accelerates the prediction of diblock copolymer morphology evolution for large domains over long timescales, where direct particle-based simulations are resource intensive.\"},{\"question\":\"How does the method differ from empirical continuum models?\",\"answer\":\"It learns stochastic, defect-annihilation processes directly from particle-based simulations rather than relying on empirical continuum descriptions.\"},{\"question\":\"What boundary conditions can the UNet model support?\",\"answer\":\"The architecture respects different boundary conditions, enabling periodic and fixed substrate boundary conditions of arbitrary shape.\"}]","Prediction of Diblock Copolymer Morphology via Machine Learning | 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problem does the machine learning approach aim to solve?","Question",{"text":75,"@type":76},"It accelerates the prediction of diblock copolymer morphology evolution for large domains over long timescales, where direct particle-based simulations are resource intensive.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the method differ from empirical continuum models?",{"text":80,"@type":76},"It learns stochastic, defect-annihilation processes directly from particle-based simulations rather than relying on empirical continuum descriptions.",{"name":82,"@type":73,"acceptedAnswer":83},"What boundary conditions can the UNet model support?",{"text":84,"@type":76},"The architecture respects different boundary conditions, enabling periodic and fixed substrate boundary conditions of arbitrary 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