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SUMMARY:Data-driven ML-enhanced approaches to support accelerated material
 s design for extreme conditions - Prof. Lori Graham-Brady\, Johns Hopkins 
 University
DTSTART:20231201T140000Z
DTEND:20231201T150000Z
UID:TALK208555@talks.cam.ac.uk
CONTACT:Burigede Liu
DESCRIPTION:Machine learning and AI-driven approaches to evaluating materi
 als are highly efficient but can suffer from reduced accuracy and interpre
 tability that is provided by physics-based computational solutions\; howev
 er\, these results may be sufficient to identify material chemistries and 
 microstructures that merit further exploration. Such data-driven approache
 s are enabled by recent advances in high-throughput experimental technique
 s that offer exciting opportunities to generate statistically significant 
 quantities of materials characterization data. Similar trade-offs are foun
 d in high-throughput experiments\, which may miss some of the relevant phy
 sics but provide an assessment of whether material performance changes whe
 n moving from one specimen to another. By providing a rapid evaluation of 
 new materials\, machine learning models support accelerated screening and 
 decision-making for control and optimization of high-throughput processes 
 on the path to materials design. This talk will provide an overview of the
  AI for Materials Design (AIMD) facility at Johns Hopkins\, which highligh
 ts some of the challenges\, pitfalls and opportunities inherent in an inte
 grated high-throughput and automated materials design framework\, in parti
 cular addressing challenges associated with assessing high-temperature\, h
 igh-rate and high-pressure environments. The role of machine learning mode
 ls in guiding this automated materials design is highlighted and discussed
  in the context of a few example applications. 
LOCATION:Oatley Seminar Room\, Department of Engineering
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