Alexander Pak

Elucidating the relationships between microscopic interactions and macroscopic behavior of self-assembled soft and biological matter has led to numerous advances across energy, sustainability, and healthcare. Many such systems exhibit hierarchical structures undergoing morphological transitions, often under out-of-equilibrium conditions. However, it remains largely unknown how collective molecular reorganization may be modulated, which, in turn, may regulate macroscopic functionality. Throughout this talk, I will explore this theme for two self-organizing complexes by leveraging our computational approach that combines coarse-grained modeling, which emulates the behavior of these systems under reduced representations (i.e., simplified degrees of freedom), with data-driven approaches. First, I will demonstrate this framework in the context of porous crystals, three-dimensional materials consisting of an ordered network of nodes (or clusters) connected by organic ligands through coordination bonds, with a wide range of applications across separations, catalysis, and optoelectronics. In particular, I will show how we can study the spatiotemporal evolution of porous crystal synthesis via self-assembly with an emphasis on transient intermediate phases and kinetically accessible assembly pathways. Second, I will discuss two-dimensional para-crystalline lattices composed of surface-layer proteins where controlled disassembly is of interest as an anti-virulence strategy. Using a combination of molecular dynamics simulations, machine learning, and explainable artificial intelligence methods, I will reveal mechanisms that therapeutics can target to promote surface-layer depolymerization and aid in the fight against antibiotic resistant bacteria. Finally, I will share our most recent work on developing new machine-learning-enabled coarse-grained models to explicitly identify kinetically-driven morphological transitions during self-organization. The insights from these studies reveal the importance of a dynamical perspective on structure-function relationships and highlight the utility of multiscale simulations and statistical inference methods.

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 received his B.S. in Chemical Engineering from M.I.T and his Ph.D. in Chemical Engineering from UT Austin. His graduate research focused on fundamental charge storage mechanisms using carbon-based nanomaterials for supercapacitor applications. As a postdoc, Alex received the F32 NIH Postdoctoral National Research Service Award, which supported his transition into computational biophysics as part of the Chemistry Department at the University of Chicago. His group focuses on the development of multiscale simulation techniques and their application toward both fundamental understanding and engineered control of self-assembled complexes, including for macromolecules, polymers, and porous crystals. 

• Time: 11:00 AM
• Location: 206 Furnas Hall