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UID:0-8319@eng.ufl.edu
DTSTART;TZID=America/New_York:20260331T090000
DTEND;TZID=America/New_York:20260331T100000
DTSTAMP:20260306T193254Z
URL:https://www.eng.ufl.edu/news-events/events/che-2026-spring-seminar-ser
 ies-alexander-pak-phd/
SUMMARY:ChE 2026 Spring Seminar Series - Alexander Pak\, PhD
DESCRIPTION:Speaker: Alexander Pak\, PhD\nAssistant Professor in the Depart
 ment of Chemical and Biological Engineering\, Quantitative Biosciences and
  Engineering Program\, and Materials Science Program \nColorado School of 
 Mines \n\nTitle: Mechanisms of Morphological Transitions in Self-Organizin
 g Molecular Systems \n\nAbstract: Elucidating how microscopic molecular in
 teractions give rise to macroscopic function remains a central challenge i
 n soft and biological materials. Many self-organizing supra- and macromole
 cular systems admit multiple accessible morphologies under similar chemica
 l conditions. Subtle changes in interaction strength\, flexibility\, or en
 vironment can redirect assembly or trigger disassembly\, leading to distin
 ct structural and functional outcomes. In this context\, the traditional s
 tructure-function paradigm is incomplete without a dynamical perspective o
 n how collective molecular motions govern morphology selection. In this se
 minar\, I will present a multiscale computational framework\, augmented by
  data-driven analysis\, to uncover the thermodynamic and dynamical mechani
 sms underlying morphological transitions in select systems. I will show ho
 w effective free energy landscapes derived from quantum-to-coarse-grained 
 simulations identify thermodynamic windows and pathways for metal-organic 
 framework assembly. I will then demonstrate how competing morphologies in 
 bacterial microcompartment shell proteins are selected through linker-medi
 ated collective dynamics and how machine learning applied to molecular dyn
 amics trajectories reveals motion-dependent mechanisms that promote contro
 lled depolymerization of surface-layer lattices. Finally\, I will introduc
 e machine-learning-enhanced coarse-grained models that preserve structural
  fidelity while retaining key dynamical signatures\, which will enable sys
 tematic distinction between thermodynamically and kinetically governed tra
 nsitions. Together\, these studies illustrate how integrating statistical 
 mechanics with data-driven modeling provides mechanistic insight into stru
 cture-dynamics-function relationships and establishes a foundation for pre
 dictive control of morphology in complex soft materials. \n\nBio: Alex is 
 currently an Assistant Professor of Chemical and Biological Engineering at
  Colorado School of Mines\, where he has been since January of 2021. He re
 ceived his B.S. in Chemical Engineering from M.I.T and his Ph.D. in Chemic
 al Engineering from UT Austin. His graduate research focused on fundamenta
 l charge storage mechanisms using carbon-based nanomaterials for supercapa
 citor applications. As a postdoc\, Alex received the F32 NIH Postdoctoral 
 National Research Service Award\, which supported his transition into comp
 utational biophysics as part of the Chemistry Department at the University
  of Chicago. Alex is the recipient of the R35 MIRA award from the NIH. His
  group focuses on the development of multiscale simulation techniques and 
 their application toward both fundamental understanding and engineered con
 trol of self-assembled complexes\, including for macromolecules\, polymers
 \, and porous crystals.  
CATEGORIES:Seminars
LOCATION:NEB 202\, 1064 CENTER DR GAINESVILLE\, FL 32611 Bldg #: 0033\, Gai
 nesville\, Florida\, 32611\, United States
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