[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122697-en":3,"doc-seo-122697-105":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},122697,549758146520,"Patrick","https://ap-avatar.wpscdn.com/avatar/80002397d8c0411e94?_k=1775819394049821470",8,"Research & Report","Characterizing Different Motility Induced Regimes in Active Matter with Machine Learning and Noise","The study characterizes motility-induced phase separation (MIPS) in two-dimensional run-and-tumble disk systems using machine learning and noise fluctuation analysis. Results indicate multiple distinct MIPS regimes depending on density and run time, including an active fluid, an active crystal, and a critical regime. Regimes are detected via an order parameter from principal component analysis combined with cluster stability, and machine learning outperforms standard structural measures like maximum cluster size. Noise power spectra of average-speed fluctuations also distinguish regimes, with a maximum and 1/f^1.6 signature in the critical regime.","arXiv :2308 .07296v1 [ cond-mat .soft] 14 Aug 2023  \nCharacterizing Different Motility Induced Regimes in Active Matter with Machine  \nLearning and Noise  \nD. McDermott 1 , C. Reichhardt2 , and C. J. O. Reichhardt2  \n1 X-Theoretical Design Division, Los Alamos National Laboratory,  \nLos Alamos, New Mexico 87545 USA  \n2 Theoretical Division, Los Alamos National Laboratory,  \nLos Alamos, New Mexico 87545 USA  \n(Dated: August 15, 2023)  \nWe examine motility-induced phase separation (MIPS) in two-dimensional run and tumble disk systems using both machine learning and noise fluctuation analysis. Our measures suggest that within the MIPS state there are several distinct regimes as a function of density and run time, so that systems with MIPS transitions exhibit an active fluid, an active crystal, and a critical regime. The different regimes can be detected by combining an order parameter extracted from principal component analysis with a cluster stability measurement. The principal component-derived order parameter is maximized in the critical regime, remains low in the active fluid, and has an intermediate value in the active crystal regime. We demonstrate that machine learning can better capture dynamical properties of the MIPS regimes compared to more standard structural measures such as the maximum cluster size. The different regimes can also be characterized via changes in the noise power of the fluctuations in the average speed. In the critical regime, the noise power passes through a maximum and has a broad spectrum with a 1/f1.6 signature, similar to the noise observed near depinning transitions or for solids undergoing plastic deformation.  \nI. INTRODUCTION  \nActive matter denotes systems composed of selfpropelling agents or particles that move using internal driving or energy harvested from the surrounding environment 1–3 . Examples of active matter include bacteria4 , engineered systems such as robots5 , and colloidal particles that move using a variety of mechanisms3,6,7 such as light8,9 or magnets 10 . One of the most studied phenomena found in models of interacting active particles, such as active disks, is motility-induced phase segregation (MIPS), which occurs even for systems with only repulsive interactions when the persistence of the motion is large enough. In MIPS, for densities well below those at which the system can uniformly crystallize, the system phase-separates into a dense or crystalline phase coexisting with a low density gas 11 . Although MIPS has generally been regarded as a single phase6,8,12,13 , its intrinsically dynamic nature means that there could be different dynamic regimes or changing structures within the MIPS state.  \nVarious studies have noted interruptions to MIPS due to mechanisms such as polar alignment between neighboring particles 14 , inertia 15 , or large-scale shear motions due to the high speed of individual particles 16 . Work on low-density, apolar active matter has shown the cluster size and average speed of active colloids exhibit power law distributions 17 . Experiments have revealed a variety of phases in suspensions of bacteria4 and run-and-tumble Quincke rollers 18 , though in these systems the hydrodynamics of the suspending fluids may play a role. Furthermore, torque can mediate an active clustered phase distinct from MIPS 19 . In very dense active matter, different  \nkinds of intermittency have been found as a function of activity when the system is a solid20 .  \nTo characterize MIPS, a number of traditional measurements are commonly used that have been adapted from equilibrium systems, such as average cluster size, the radial distribution function, or the amount of bond orientational order21 . Such measures were designed to distinguish a variety of transitions including short versus long-range order or order-order structural phase transitions; however, these measures may fail to detect notable features due to dynamic changes or the fluctuating inhomogeneity of particle ","cbCaidy8EnT5g2il","https://ap.wps.com/l/cbCaidy8EnT5g2il","pdf",1409502,1,11,"English","en",105,"# Introduction\n## Active matter and MIPS background\n## Traditional characterization methods and limitations\n## Machine learning for nonequilibrium phases\n## Noise fluctuation characterization and power spectra","[{\"question\":\"What systems and phenomenon are analyzed in the document?\",\"answer\":\"The document analyzes motility-induced phase separation (MIPS) in two-dimensional run-and-tumble disk systems within active matter.\"},{\"question\":\"How are different MIPS regimes identified?\",\"answer\":\"Different regimes are detected by combining an order parameter extracted from principal component analysis with a cluster stability measurement, and machine learning is used to capture dynamical properties.\"},{\"question\":\"What role does noise play in characterizing the critical regime?\",\"answer\":\"Noise power of fluctuations in the average speed distinguishes regimes: in the critical regime the noise power reaches a maximum and shows a broad spectrum with a 1/f^1.6 signature.\"}]","Characterizing Different Motility Induced Regimes in Active Matter with Machine Learning and Noise | 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systems and phenomenon are analyzed in the document?","Question",{"text":75,"@type":76},"The document analyzes motility-induced phase separation (MIPS) in two-dimensional run-and-tumble disk systems within active matter.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How are different MIPS regimes identified?",{"text":80,"@type":76},"Different regimes are detected by combining an order parameter extracted from principal component analysis with a cluster stability measurement, and machine learning is used to capture dynamical properties.",{"name":82,"@type":73,"acceptedAnswer":83},"What role does noise play in characterizing the critical regime?",{"text":84,"@type":76},"Noise power of fluctuations in the average speed distinguishes regimes: in the critical regime the noise power reaches a maximum and shows a broad spectrum with a 1/f^1.6 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