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UID:164@cds.iisc.ac.in
DTSTART;TZID=Asia/Kolkata:20251218T150000
DTEND;TZID=Asia/Kolkata:20251218T160000
DTSTAMP:20251204T063110Z
URL:https://cds.iisc.ac.in/events/seminar-cds-102-december-18th-1500-bridg
 ing-scales-with-machine-learning-from-first-principles-statistical-mechani
 cs-to-continuum-phase-field-computations-to-study-order-disorder-transiti/
SUMMARY:{Seminar} @ CDS: #102\, December 18th: 15:00: "Bridging scales with
  Machine Learning: From first principles statistical mechanics to continuu
 m phase field computations to study order-disorder transitions in LixCoO2.
 "
DESCRIPTION:Department of Computational and Data Sciences\nDepartment Semin
 ar\n\n\n\nSpeaker : Prof. Krishna Garikipati\, Professor of Aerospace and 
 Mechanical Engineering\, University of Southern California\nTitle : Bridgi
 ng scales with Machine Learning: From first principles statistical mechani
 cs to continuum phase field computations to study order-disorder transitio
 ns in LixCoO2\nDate &amp\; Time: December 18th\, 2025 (Thursday)\, 15:00 P
 M\nVenue : # 102\, CDS Seminar Hall\n\n\n\nABSTRACT\n\nLix {TM} O2 (TM={
 Ni\, Co\, Mn}) forms an important family of cathode materials for Li-ion b
 atteries\, whose performance is strongly governed by Li composition-depend
 ent crystal structure and phase stability. Here\, we use LixCoO2 (LCO) as
  a model system to benchmark a machine learning-enabled framework for brid
 ging scales in materials physics. We focus on assemblies of thousands of a
 toms described by density functional theory-informed statistical mechanics
  to further drive continuum phase field studies of the dynamics of order-d
 isorder transitions in LCO. Central to the scale bridging is the rigorous\
 , quantitatively accurate\, representation of the free energy density and 
 chemical potentials of this material system by coarse-graining formation e
 nergies for specific atomic configurations. We develop physics- and data-d
 riven active learning workflows to train integrable deep neural networks f
 or such high-dimensional free energy density and chemical potential functi
 ons. Additionally\, we explore Equivariant Graph Neural Networks to bypass
  traditional cluster expansion-based representations formation energies su
 bsequently used in the statistical mechanics. The resulting\, first princi
 ples-informed\, machine learning-enabled\, phase-field computations allow 
 us to study LCO cathodes' order-disorder transitions in terms of temperatu
 re\, microstructure\, and charge cycling. To the best of our knowledge\, s
 uch a scale bridging framework has not been previously demonstrated for LC
 O\, or for materials systems of comparable technological interest. This ap
 proach can be expanded to other materials systems and can incorporate addi
 tional physics to that studied here.\n\nBIO: Krishna Garikipati obtained h
 is PhD at Stanford University in 1996\, and after a few years of post-doct
 oral work\, he joined the University of Michigan in 2000\, rising to Profe
 ssor in the Departments of Mechanical Engineering and Mathematics. Between
  2016 and 2022\, he served as the Director of the Michigan Institute for C
 omputational Discovery &amp\; Engineering (MICDE). In January 2024 he move
 d to the University of Southern California as a Professor of Aerospace and
  Mechanical Engineering. His research is in scientific machine learning an
 d computational science\, with applications drawn from biophysics\, mathem
 atical biology\, materials physics and nonlinear mechanics. He has been aw
 arded the DOE Early Career Award for Scientists and Engineers\, the Presid
 ential Early Career Award for Scientists and Engineers (PECASE)\, a Humbol
 dt Research Fellowship\, and the 2025 Oden Medal in Computational Science 
 from the US Association for Computational Mechanics. He is a fellow of the
  US Association for Computational Mechanics\, the International Associatio
 n for Computational Mechanics and the Society of Engineering Science\, a L
 ife Member of Clare Hall at University of Cambridge\, and a visiting schol
 ar in Computational Biology at the Flatiron Institute of the Simons Founda
 tion.\n\nHost Faculty: Dr. Phani Motamarri\n\n\n\nALL ARE WELCOME
CATEGORIES:Events,Talks
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