[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118285-en":3,"doc-seo-118285-105":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},118285,7971461740886,"Theodore","https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2",8,"Research & Report","A snapshot review on soft-materials assembly design utilizing machine learning methods - survey","Machine-learning-driven methods are increasingly shaping how soft materials assemblies are designed, enabling workflows that go beyond earlier forward simulation and phase-diagram iteration. This snapshot review first summarizes long-established ML-free approaches for self-assembly design, then explains ML/AI-enabled methods that improve key pipeline stages, including high-throughput structural characterization and inverse design of building blocks. It also surveys scientific software developments and how they integrate with classical molecular dynamics engines such as LAMMPS and HOOMD-blue.","arXiv :2405 .03805v1 [ cond-mat .soft] 6 May 2024  \nA snapshot review on soft-materials assembly design utilizing machine learning methods  \nMaya M. Martirossyan 1 , Hongjin Du 1 , Julia Dshemuchadse 1*,  \nChrisy Xiyu Du2*  \n1 Department of Materials Science and Engineering, Cornell University, Ithaca, 14853, NY, USA.  \n2 Department of Mechanical Engineering, University of Hawai‘i at M¯anoa, Honolulu, 96822, HI, USA.  \n*Corresponding author(s). E-mail(s): [jd732@cornell.edu](jd732@cornell.edu) ; [xiyudu@hawaii.edu](xiyudu@hawaii.edu) ;  \nContributing [authors: mmm457@cornell.edu](authors: mmm457@cornell.edu); [hd329@cornell.edu](hd329@cornell.edu) ;  \nAbstract  \nSince the surge of data in materials science research and the advancement in machine learning methods, an increasing number of researchers are introducing machine learning techniques into the next generation of materials discovery, ranging from neural-network learned potentials to automated characterization techniques for experimental images. In this snapshot review, we first summarize the landscape of techniques for soft materials assembly design that do not employ machine learning or artificial intelligence and then discuss specific machine-learning and artificial-intelligence-based methods that enhance the design pipeline, such as high-throughput crystal-structure characterization and the inverse design of building blocks for materials assembly and properties. Additionally, we survey the landscape of current developments of scientific software, especially in the context of their compatibility with traditional molecular dynamics engines such as LAMMPS and HOOMD-blue.  \nKeywords: soft materials, inverse design, machine learning  \n1  \n1 Introduction  \nThe design of soft materials assemblies with targeted structures and properties requires the engineering of building blocks and interactions that can spontaneously assemble a target material. Before the upsurge of computational capabilities, many studies of soft materials assemblies followed a similar framework: identify a few parameters (building block properties, densities, etc.), run forward simulations varying the parameters, outline phase diagrams based on these parameters, and iterate. This “forward approach”has provided researchers with valuable insights and tools for exploring soft materials systems: phase diagrams for systems of hard spheres, anisotropic particles with polyhedral shapes, and block copolymers; rare-event sampling techniques; and local bond-order parameters to identify crystal motifs and structures. In recent decades, the exponential growth of computational power has widened the parameter space that can feasibly be searched, and researchers are incorporating machine learning and artificial intelligence (ML/AI) techniques to enhance their materials assembly pipelines (Fig. 1) . Not only do these advanced tools enable us to more thoroughly probe broad questions and challenges in the field—for example, the competing nature of enthalpy and entropy in determining structure formation, dynamics, and materials properties in physical systems—but they also allow for the pursuit of reverse-or inverse-design approaches enabled by numerical optimization. Moreover, the study of soft materials (i.e., composed of mesoscopic building blocks, e.g., nanoparticles, colloids, or block copolymers) serves as a coarse-grained version of nano-or atomic-scale phenomena and can aid in understanding how to manipulate and design significantly more complicated building blocks (e.g., macromolecules, such as proteins) .  \nForward approach, standard molecular dynamics engine  \nInverse-design,  \nML-enabled molecular dynamics engine  \nFig. 1 Soft materials design pipeline. Input parameters for building blocks can be patchy particles, sphere unions, and polyhedral shapes with any arbitrary pair potential functions. To quantify materials structures and properties, a variety of descriptors can be used. Here we depict bond-order paramet","cbCaiv5K5YmqDeaQ","https://ap.wps.com/l/cbCaiv5K5YmqDeaQ","pdf",1287996,1,39,"English","en",105,"# Introduction\n## Soft materials design pipeline\n## ML/AI-aided methods overview","[{\"question\":\"What does the review focus on regarding soft materials assembly design?\",\"answer\":\"It reviews technique landscapes for soft-materials assembly design, contrasting ML/AI-free methods with specific ML/AI-based approaches that enhance the design pipeline.\"},{\"question\":\"Which ML/AI methods are highlighted to improve the assembly workflow?\",\"answer\":\"The review highlights high-throughput structural characterization and inverse design of building blocks, including approaches aided by automatic differentiation for inverse design tasks.\"},{\"question\":\"How does the review address scientific software and molecular dynamics engines?\",\"answer\":\"It surveys current software developments and emphasizes compatibility with traditional molecular dynamics engines such as LAMMPS and HOOMD-blue.\"}]","A snapshot review on soft-materials assembly design utilizing machine learning methods - 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