Massachusetts Institute of Technology
As a high school student in Pittsburgh, Kevin Silmore loved the sciences – math, physics, chemistry and computers – so much he had difficulty deciding which to focus on. He also is a classical violinist, and for a time considered pursuing that as a college major.
Silmore, now a Department of Energy Computational Science Graduate Fellowship (DOE CSGF) recipient, finally landed on chemical engineering as a way to combine his interests – while playing violin as an avocation. At Princeton University he delved into high-performance computing for molecular dynamics, a technique to simulate the interactions of atoms and molecules, but also served as concertmaster for the Princeton Sinfonia and played in the university orchestra.
As a Massachusetts Institute of Technology doctoral candidate, Silmore’s research continues in the interdisciplinary vein he enjoys. With advisors James Swan and Michael Strano, he focuses on soft-matter physics, useful for modeling such things as nanotubes – submicroscopic, hollow noodles of pure carbon – and graphene, single-atom-thick carbon sheets, as they interact and move in fluids. Understanding their properties is vital to scaling-up nanomaterial production and processing for real-world applications.
“It involves a lot of physics, fluid mechanics, chemical engineering knowledge, and applied math,” plus “high-performance computing, because in order to study these systems you often need a lot of computational power.”
It’s needed because the systems he models are “a total nightmare,” Silmore laughs. “Particles are interacting with particles, particles are interacting with the fluid, and of course the fluid is mediating interactions between all the particles, too.” Meanwhile, the “objects I’m studying deform and change over time.” Quantifying this behavior is challenging.
To model these soft, “squishy” materials, Silmore taps Brownian dynamics, based in longstanding mathematical descriptions of particles’ random movements and interactions, driven by thermal fluctuations.
In a recent Journal of Chemical Physics paper, he and Swan describe using a variant of the method to accelerate soft-materials models as particles relax to equilibrium – a time-consuming process both in real life and on a computer, especially when the objects they comprise are relatively big and flexible, like many nanomaterials. The method relies on the principle that particles in fluids are more strongly coupled than those in a vacuum, leading them to reach equilibrium more quickly. Silmore and Swan took the governing equations of motion and “put them on steroids,” with added tweaks to enhance particle coupling, leading to faster relaxation.
Silmore turned the method into a plug-in for the HOOMD-blue particle simulation code, which he first encountered at Princeton and is widely used in Swan’s group. “We’re using it to look at various soft matter systems, so it’s a nice foundational tool for the rest of my research.”
Silmore applies that and other techniques to his goal of understanding the underlying physics of how fluid flow affects chains – such as the large, repeating molecular units comprising polymers – and sheets, such as graphene. “What happens as a function of the material properties, and by that, I mean, how bendy is it? How stretchy is it? And how do these properties ultimately affect physical behavior?” he says. “My eye is on how we can use this knowledge to design ways to control these materials in flow,” leading to new methods to produce functional nanomaterials.
Silmore’s 2019 Oak Ridge National Laboratory practicum also involved modeling soft matter – specifically systems with charged polymers. Batteries and other energy-related devices use charged polymers, so understanding their microscopic behavior is vital to creating improved materials for these applications.
With Rajeev Kumar and Bobby Sumpter in the lab’s Center for Nanophase Materials Science, Silmore learned new modeling techniques, such as self-consistent field theory and continuum methods. Both involve modeling polymers as continuous fields of matter rather than collections of discrete particles. With the center’s CADES computing cluster, he used the methods on several problems related to the behavior of charged polymers in solution and thin films. Often the work involved collaborating with scientists to explain results they had gathered in their labs. By grasping the physics more deeply, “we were able to understand the actual experimental data better as well.”
Silmore still works with Kumar and Sumpter, with one paper recently published in Science Advances and two more in progress. More importantly, the methods he learned provided new ways to consider his research at MIT.
Silmore expects to graduate in 2021 and is leaning toward pursuing an academic post. He recently earned departmental and school of engineering awards for his teaching assistant work in a core graduate ChemE course.
“I love teaching,” he says, “and I want to solve important and interesting problems.”
Image caption: A snapshot of an equilibrium simulation of flexible polymer chains using Collective Mode Brownian Dynamics. Credit: Kevin Silmore.