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X-WR-CALNAME:Engineering Seminar: Machine Learning Enhanced Multiscale Mate
 rial Mechanics Models for Complex Alloys
X-WR-TIMEZONE:Central Time (US & Canada)
BEGIN:VEVENT
DTSTAMP:20260917T044107Z
UID:tag:localist.com\,2008:EventInstance_47659000560103
DTSTART:20241018T180000Z
DTEND:20241018T190000Z
DESCRIPTION:Dr. Lin Li\, Associate Professor in School for Engineering of M
 atter\, Transport and Energy at Arizona State University\, Tempa will give
  a seminar titled "Machine Learning Enhanced Multiscale Material Mechanics
  Models for Complex Alloys" to the interested faculty and students at Disc
 overy Park.\n\n \n\nAbstract\n\nComplex alloys\, including both structural
 ly disordered metallic glass and chemically disordered high entropy alloys
 \, have received considerable attention because they provide a broad range
  of opportunities for property control\, and have found applications in st
 ructural materials\, damage resistance\, and many other functionalities. T
 he development of the predictive materials mechanics model is challenging 
 due to the complex atomic environments and non-equilibrium state of matter
 s that are dynamically evolving. For the structural disorder alloys\, I wi
 ll present a multiscale material mechanics model\, incorporating atomic-le
 vel disordered features in a coarse-grained model that enables collective 
 deformation and macroscopic mechanical behaviors to be modeled in the meta
 llic glasses. Machine learning models have been used to parameterize atomi
 c flow defects to inform the coarse-grained model. Emphasis will be placed
  on the influence of nanoscale heterogeneity due to the atomic short-range
  to medium-range orders on the large-scale shear banding behaviors. For th
 e chemical disorder alloys\, I will focus on high entropy alloys (HEAs) th
 at are formed by mixing equal or relatively large portions of multiple ele
 ments. Both experimental and computational evidence suggest the existence 
 of preferred atomic pairs or chemical ordering in many HEAs. We have devel
 oped a machine learning potential in the following hybrid molecular dynami
 cs/Monte Carlo simulations to elucidate the complicated interplay between 
 local ordering\, phase stability\, dislocation behaviors\, mechanical prop
 erties in model NbMoTaW HEAs. The approach with machine learning-enhanced 
 multiscale material mechanics models can accelerate the design and discove
 ry of high-performance structural materials in a vast configurational and 
 compositional space.\n\n \n\nBio\n\nDr. Lin Li is currently an associate p
 rofessor in the School for Engineering of Matter\, Transport\, and Energy 
 at Arizona State University (ASU). Dr. Li received her Ph.D. degree in Mat
 erials Science and Engineering from The Ohio State University in 2011. The
 reafter\, she worked as a postdoctoral associate at the Massachusetts Inst
 itute of Technology. Prior to her current position at ASU\, Dr. Li held th
 e roles of assistant professor and tenured associate professor at the Univ
 ersity of Alabama. Her research interest is structure-property-processing 
 relationships in advanced structural metals and materials for extreme envi
 ronments\, with emphasis on size effect\, structural disorder\, interfaces
 \, and mechanics. Her research utilizes multiscale modeling and experiment
 ation\, statistical tools\, and machine learning models to establish the c
 onnections between microscopic atomic processes and macroscopic material p
 erformance\, particularly for nanostructured alloys\, metallic glasses\, a
 nd high entropy alloys. Dr. Li is the recipient of the Ralph E. Powe Junio
 r Faculty Enhancement Award from Oak Ridge Associated Universities\, and t
 he 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 Administration (NASA).
GEO:33.253134;-97.148579
LOCATION:Discovery Park Building\, K150
SUMMARY:Engineering Seminar: Machine Learning Enhanced Multiscale Material 
 Mechanics Models for Complex Alloys
URL;VALUE=URI:https://calendar.unt.edu/event/es-202410181300
CATEGORIES:Lectures & Speakers
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