BEGIN:VCALENDAR VERSION:2.0 CALSCALE:GREGORIAN PRODID:iCalendar-Ruby BEGIN:VEVENT CATEGORIES:Lectures & Speakers DESCRIPTION:Dr. Lin Li\, Associate Professor in School for Enginnering of M atter\, Transport and Energy at Arizona State University\, Tempa will give a seminar titled "Machine Learning Enhanced Multiscale Material Mechanics M odels for Complex Alloys" to the interested faculty and students at Discove ry Park.\n\n \n\nAbstract\n\nComplex alloys\, including both structurally d isordered metallic glass and chemically disordered high entropy alloys\, ha ve received considerable attention because they provide a broad range of op portunities for property control\, and have found applications in structura l materials\, damage resistance\, and many other functionalities. The devel opment of the predictive materials mechanics model is challenging due to th e complex atomic environments and non-equilibrium state of matters that are dynamically evolving. For the structural disorder alloys\, I will present a multiscale material mechanics model\, incorporating atomic-level disorder ed features in a coarse-grained model that enables collective deformation a nd macroscopic mechanical behaviors to be modeled in the metallic glasses. Machine learning models have been used to parameterize atomic flow defects to inform the coarse-grained model. Emphasis will be placed on the influenc e of nanoscale heterogeneity due to the atomic short-range to medium-range orders on the large-scale shear banding behaviors. For the chemical disorde r alloys\, I will focus on high entropy alloys (HEAs) that are formed by mi xing equal or relatively large portions of multiple elements. Both experime ntal and computational evidence suggest the existence of preferred atomic p airs or chemical ordering in many HEAs. We have developed a machine learnin g potential in the following hybrid molecular dynamics/Monte Carlo simulati ons to elucidate the complicated interplay between local ordering\, phase s tability\, dislocation behaviors\, mechanical properties in model NbMoTaW H EAs. The approach with machine learning-enhanced multiscale material mechan ics models can accelerate the design and discovery of high-performance stru ctural materials in a vast configurational and compositional space.\n\n \n\ nBio\n\nDr. Lin Li is currently an associate professor in the School for En gineering of Matter\, Transport\, and Energy at Arizona State University (A SU). Dr. Li received her Ph.D. degree in Materials Science and Engineering from The Ohio State University in 2011. Thereafter\, she worked as a postdo ctoral associate at the Massachusetts Institute of Technology. Prior to her current position at ASU\, Dr. Li held the roles of assistant professor and tenured associate professor at the University of Alabama. Her research int erest is structure-property-processing relationships in advanced structural metals and materials for extreme environments\, with emphasis on size effe ct\, structural disorder\, interfaces\, and mechanics. Her research utilize s multiscale modeling and experimentation\, statistical tools\, and machine learning models to establish the connections between microscopic atomic pr ocesses and macroscopic material performance\, particularly for nanostructu red alloys\, metallic glasses\, and high entropy alloys. Dr. Li is the reci pient of the Ralph E. Powe Junior Faculty Enhancement Award from Oak Ridge Associated Universities\, and the Air Force Summer Faculty Fellowship. Her research has been sponsored by the National Science Foundation (NSF)\, the Department of Energy (DOE)\, and the National Aeronautics and Space Adminis tration (NASA). DTEND:20241018T190000Z DTSTAMP:20260509T204335Z DTSTART:20241018T180000Z GEO:33.253134;-97.148579 LOCATION:Discovery Park Building\, K150 SEQUENCE:0 SUMMARY:Engineering Seminar: Machine Learning Enhanced Multiscale Material Mechanics Models for Complex Alloys UID:tag:localist.com\,2008:EventInstance_47659000560103 URL:https://calendar.unt.edu/event/es-202410181300 END:VEVENT END:VCALENDAR