(2jn) Creating a Toolbox for Studying Soft Material Self-Assembly and Dynamics

Simulation of soft materials such as polymers is vital for providing both the physical insights and rapid data generation necessary to develop new sustainable materials which perform comparably to current material choices. However, separation of timescales in activated processes such as self-assembly makes obtaining adequate sampling of material properties in computer simulations impossible through brute force alone. By using advanced sampling and free energy techniques it is possible to expand the range of timescales currently accessible by computer simulation and make study of these materials and processes possible. My research focuses on furthering simulation capabilities through advanced sampling methods in order to design sustainable mechanically strong polymers.

As a faculty member I seek to extend our ability to control the dynamics and self-assembly pathways of complex polymeric materials. For these materials the assembly process relies on collective motion of individual polymer chains that allow for larger scale collective reorganizations. Utilizing new methods based upon recent advances in machine learning and data analysis techniques I will identify and characterize the collective processes that govern these reorganizations. Identification of these collective modes will enable the design of new material design pathways and control of these materials. The computational tools developed will additionally apply beyond just polymeric systems and can be used to assist in designing kinetic pathways in other soft materials such as proteins and colloidal assembled structures.

During my time as a postdoc at the University of Chicago my work has focused on predicting polymer properties through machine learning and developing novel methods for simulation of thermoplastic elastomers. Using a model diblock copolymer with a variable sequence block, I created a dataset containing information about the chi parameter, sequence, and lamellar period. Using a combination of dimensionality reduction techniques and machine learning methods this data set was used to show that ML models can be applied to such a dense polymer system to predict desired properties, as well as provide insights into heuristics to describe the underlying behavior.

My studies of thermoplastic elastomers focused on two distinct issues with their simulation. First, because of their bridged nature, these structures take a tremendously long time to relax. Second, currently the relationship of various topological structures to mechanical properties remains unmapped. To overcome the first issue, I developed advance sampling methods to bring the system to true equilibrium via a novel type of rebridging move. In order to better quantify the mechanical response, I studied the behavior of the bridging rubbery domain with different connectivities and topological features. To obtain computational resources necessary for this work I co-wrote and obtained an ASCR Leadership Computing Challenge grant for usage with the upcoming Polaris cluster located at Argonne National Lab.

As a graduate student at the University of Minnesota my work focused on the dynamic properties of micellar surfactant systems. Using coarse grained simulation models, I determined the adsorption and self-assembly properties of a model surfactant. In order to do so, I developed methods to determine the micelle free energy as a function of aggregation number, as well as insertion and expulsion rates of surfactant to and from micelles. These parameters were used in a numerical model to determine long time behaviors beyond what could be reached in particle-based simulation and led to confirmation of several regimes of surfactant behavior.

Teaching Interests

As a faculty member I have a duty to assist in education of the next generation of chemical engineers. My primary teaching interests are thermodynamics, mass and energy transfer, statistical mechanics, and introductory mass and energy balance courses. While the first three topics my interest stems from their relation to my research, my interest in teaching the last topic stems from a fervent desire to ensure that young chemical engineers are given an understanding the most fundamental tools they will use throughout their careers. I am prepared to teach any chemical engineering course at the undergraduate level. At the graduate level I am qualified to teach courses concerning thermodynamics, statistical mechanics, transport, polymer physics, and colloid and interface science. In addition to direct teaching I am a strong believer in mentoring at the undergraduate, graduate, and post graduate level. During my postdoc at the University of Chicago I led a regular discussion session and lecture focusing on polymer physics for new graduates students in the group, as well as served as a co-instructor for a course on molecular simulation techniques.