(6ft) Computational Assembly Engineering for Bio-Inspired Nanomaterials | AIChE

(6ft) Computational Assembly Engineering for Bio-Inspired Nanomaterials


Nguyen, T. - Presenter, Northwestern University
Research Interests: My objective is to establish a world-class research program that stands at the intersection of biophysics, chemistry, computer science and data science, aimed at transformative approaches for engineering novel nanomaterials and nanodevices for medical diagnosis, drug delivery and renewable energy applications. Specifically, I am interested in exploring design rules for materials that resemble biological matter in that they are responsive to external stimuli in a controlled matter. The design rules being targeted here include not only the geometry of assembling building blocks (such as macromolecules and colloids) and the anisotropic interactions between them, but also the assembly pathways towards desirable structures and properties away from equilibrium. The fundamental challenges to the proposed approaches are that the interactions between the assembling building blocks necessarily span multiple energy and length scales. Furthermore, I am interested in nonequilibrium assembly processes occurring at the liquid-liquid interfaces and in the presence of external fields, where available theoretical predictions have been limited, yet experimental studies can be used for verification.

To address the fundamental challenges associated with the proposed approaches, I am employing multiscale modeling and large-scale computer simulation, and developing techniques borrowed from multiple disciplines. These techniques include GPU-accelerated force fields and enhanced sampling methods for molecular simulation, large deviation theory for studies of nonequilibrium steady states, and machine learning techniques for analysis of big data sets. Particularly, my near-term interest focuses on predictive design of charged copolymers for programmable nanomaterials that are used for protein delivery and energy storage applications. The outcomes of my research program would be qualitative and quantitative predictions including, but not limited to, 1) optimal polyelectrolyte architectures and compositions, and 2) efficient routes towards desirable chemical and mechanical properties of the materials, to facilitate future experimental and theoretical investigations in the field. My extensive expertise in soft matter physics, physical modeling of polyelectrolytes, proteins and liquid-liquid interfaces, and high performance computing allows me to pursue the proposed research program successfully. Furthermore, my long-time experience with algorithm and method development for large-scale open source software packages such as LAMMPS, SSAGES and HOOMD-Blue enables me develop and optimize all the codes necessary for the studies of interest.

Teaching Interests: I am interested in teaching Chemical Engineering core courses including Fluid Flow, Process Dynamics and Control, Thermodynamics and Transport. I am also developing my own courses on Polymer Physics, Intro to Machine Learning for Chemical Engineers, and Computational Nanoscience at graduate level. I would like to facilitate cooperative learning in classroom and incorporate practical aspects with fundamental problems into lecture materials. My objectives are to explain to the students why the coursework benefit them with their career, and more importantly, to inspire them to go further with the topics covered in my courses.