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SUMMARY:CESS seminar : ML-enhanced approaches to help accelerate materials
  design for extreme environments
DTSTART:20240315T121500
DTEND:20240315T131500
DTSTAMP:20260916T003931Z
UID:b767c3a3a3d720f512f114d9b981d28015f5971a59ce143b45fd3deb
CATEGORIES:Conferences - Seminars
DESCRIPTION:Prof. Lory Brady Graham-Brady\, Johns Hopkins University\nAbst
 ract\nMachine learning and AI-driven approaches to evaluating materials pr
 ovide a highly efficient alternative to physics-based computational modeli
 ng\; however\, they often suffer from reduced accuracy and limited interpr
 etability. Even with these potential limitations\, the results may be suff
 icient in materials design to identify material chemistries and microstruc
 tures that merit further exploration. Such data-driven approaches are enab
 led by recent advances in high-throughput experimental techniques that off
 er exciting opportunities to generate statistically significant quantities
  of materials characterization data. Similar trade-offs are found in high-
 throughput experiments\, which may miss some of the relevant physics but p
 rovide an assessment of whether material performance changes when moving f
 rom one specimen to another. By providing a rapid evaluation of new materi
 als\, machine learning models support accelerated screening and decision-m
 aking for control and optimization of high-throughput processes on the pat
 h to materials design. This talk will provide an overview of the AI for Ma
 terials Design (AIMD) facility at Johns Hopkins\, which highlights some of
  the challenges\, pitfalls and opportunities inherent in an integrated hig
 h-throughput and automated materials design framework\, in particular addr
 essing challenges associated with assessing high-temperature\, high-rate a
 nd high-pressure environments. The role of machine learning models in guid
 ing this automated materials design is highlighted and discussed in the co
 ntext of a few example applications.\n\nShort bio\nLori Graham-Brady is a 
 Professor and former Chair of the Civil and Systems Engineering Department
  at Johns Hopkins University\, with secondary appointments in Mechanical E
 ngineering and Materials Science & Engineering. Her research interests are
  in AI for materials design\, computational stochastic mechanics\, multisc
 ale modeling of materials with random microstructure and the mechanics of 
 failure under high-rate loading. She is the Director of the Center on AI f
 or Materials in Extreme Environments\, Associate Director of the Hopkins E
 xtreme Materials Institute and previous Director of both an NSF-funded IGE
 RT training program with the theme of Modeling Complex Systems\, and the C
 enter for Materials in Extreme Dynamic Environments. She has received a nu
 mber of awards\, including the Presidential Early Career Awards for Scient
 ists and Engineers (PECASE)\, the Walter L. Huber Civil Engineering Resear
 ch Prize\, and the William H. Huggins Award for Excellence in Teaching.\n\
 nSandwiches are offered at the end of the seminar.\n\n 
LOCATION:GC B1 10 https://plan.epfl.ch/?room==GC%20B1%2010 https://epfl.zo
 om.us/j/67274026161
STATUS:CONFIRMED
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