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The work presents an integrated framework using CG-MD simulations, automated image-based microstructure analysis, and supervised machine learning to predict quantitative morphology descriptors. A large dataset is processed to extract domain spacing, interface length, thickness, and periodicity. Regression models forecast key morphology metrics across a broad parameter space, and feature attribution highlights interaction strength and chain length as dominant contributors for global and local descriptors.",{"@graph":14,"@context":73},[15,34,56],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/research-report/","Research & Report",3,{"item":32,"name":10,"@type":21,"position":33},"https://docshare.wps.com/document/data-driven-prediction-of-block-copolymer-morphology-using-coarse-grained-modeling-and-machine-learning/291104/",4,{"url":32,"name":10,"@type":35,"image":36,"author":41,"headline":10,"publisher":44,"fileFormat":47,"inLanguage":8,"description":12,"dateModified":48,"datePublished":49,"encodingFormat":47,"isAccessibleForFree":50,"interactionStatistic":51},"DigitalDocument",{"url":37,"@type":38,"width":39,"height":40},"https://docshare.wps.com/thumbnails/data-driven-prediction-of-block-copolymer-morphology-using-coarse-grained-modeling-and-machine-learning/291104.png","ImageObject",300,407,{"name":42,"@type":43},"Theodora","Person",{"url":19,"name":45,"@type":46},"DocShare","Organization","application/pdf","2026-10-30","2026-09-17",true,{"@type":52,"interactionType":53,"userInteractionCount":55},"InteractionCounter",{"@type":54},"ViewAction",12,{"@type":57,"mainEntity":58},"FAQPage",[59,65,69],{"name":60,"@type":61,"acceptedAnswer":62},"Why is predicting block copolymer morphology challenging?","Question",{"text":63,"@type":64},"Self-assembly depends on multiple molecular and thermodynamic parameters, such as chain architecture, interaction strength, and processing conditions. This creates a complex, high-dimensional mapping from system variables to morphology outcomes.","Answer",{"name":66,"@type":61,"acceptedAnswer":67},"What integrated framework is proposed to predict BCP morphology?",{"text":68,"@type":64},"The framework combines coarse-grained molecular dynamics simulations, automated image-based microstructure analysis, and supervised machine learning. It uses simulation-generated morphologies to train models on physically meaningful descriptors.",{"name":70,"@type":61,"acceptedAnswer":71},"Which morphology descriptors does the workflow extract and predict?",{"text":72,"@type":64},"The pipeline extracts descriptors including domain spacing, interface length, thickness, and structural periodicity. 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Predicting these morphologies remains challenging because the selfassembly process depends on multiple molecular and thermodynamic parameters, including chain architecture, interaction strength, and processing conditions. In this work, we develop an integrated computational framework combining coarse-grained molecular dynamics (CG-MD) simulations, automated image-based microstructure analysis, and supervised machine learning (ML) to predict quantitative morphological descriptors of BCP systems. A large dataset of simulation-generated morphologies is analyzed using an automated pipeline to extract physically meaningful descriptors, including domain spacing, interface length, thickness, and structural periodicity. ML regression models trained on simulation dataset accurately predict key morphology metrics across a broad parameter space. Feature attribution analysis further shows that the learned descriptor–parameter relationships recover physically expected trends, with interaction strength and chain length emerging as dominant contributors across several global and local morphology descriptors. This framework establishes a scalable strategy for linking simulationderived microstructures with data-driven prediction, enabling accelerated high-throughput design and interpretation of BCPself-assembly.  \n1 | Introduction  \nBlock copolymers (BCPs) represent a prototypical class of selfassembling soft materials, recognized for their inherent ability to spontaneously form various ordered nanostructures, such as spheres, cylinders, gyroid phases, and lamellae [1, 2]. This exceptional morphological versatility underlies diverse applications in nanolithography, controlled drug delivery, energy storage, and flexible electronics [3] . The equilibrium morphologies and associated functional properties of BCPs result from the subtle interplay among molecular factors—chemical composition, block volume fraction, molecular weight, and chain architecture—as well as external conditions including temperature, pressure, and solvent quality [4–6]. The identification of increasingly complex morphologies, including Frank–Kasper phases and hierarchical  \nnanostructures, has expanded the BCP design space, emphasizing both the opportunities and challenges inherent in exploring high-dimensional parameter landscapes controlling self-assembly [7, 8] .  \nDespite the remarkable diversity of BCP morphologies and applications, understanding and predicting their self-assembly behavior remains a formidable challenge, particularly when addressing complex systems beyond idealized theoretical assumptions [9] . Conventional approaches linking BCP composition and morphology have predominantly relied on constructing phase diagrams parameterized by key variables, including the Flory–Huggins interaction parameter (χ) [10], degree of polymerization (N) [11], and block volume fraction (f ) [12] . Although self-consistent field theory (SCFT) and its extensions enable  \nThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.  \n© 2026 The Author(s). Journal of Polymer Science published by Wiley Periodicals LLC.  \nJournal of","cbCaiq8L18D7JigR","https://ap.wps.com/l/cbCaiq8L18D7JigR","pdf",3472899,13,"English","# Introduction\n## Background on BCP morphologies and applications\n## Conventional phase-diagram approaches and limitations\n## Motivation for data-driven prediction","[{\"question\":\"Why is predicting block copolymer morphology challenging?\",\"answer\":\"Self-assembly depends on multiple molecular and thermodynamic parameters, such as chain architecture, interaction strength, and processing conditions. This creates a complex, high-dimensional mapping from system variables to morphology outcomes.\"},{\"question\":\"What integrated framework is proposed to predict BCP morphology?\",\"answer\":\"The framework combines coarse-grained molecular dynamics simulations, automated image-based microstructure analysis, and supervised machine learning. It uses simulation-generated morphologies to train models on physically meaningful descriptors.\"},{\"question\":\"Which morphology descriptors does the workflow extract and predict?\",\"answer\":\"The pipeline extracts descriptors including domain spacing, interface length, thickness, and structural periodicity. ML regression then predicts key morphology metrics across a broad parameter space.\"}]","Data-Driven Prediction of Block Copolymer Morphology Using Coarse-Grained Modeling and Machine Learning | PDF",33]