(7it) Computational Design and Characterization of Nanoscale Materials for Energy Applications

Transitioning the global infrastructure from reliance on fossil fuels to clean and sustainable sources of energy is the defining technological challenge of our age. Solving this problem will require the development of more efficient novel materials for energy production, transport, and storage. My research interests involve using a combination of first principles and statistical methods to predict mechanical, thermodynamic, and electronic properties of materials to accelerate the discovery of new materials with broad applications related to energy. I am also interested in methodological developments to increase the speed and efficiency of these calculations.

High-performance computing coupled with a variety of theoretical frameworks, including density functional theory and Monte Carlo methods, allows us to design and characterize new materials much faster than could be achieved using only experiments. Theoretical calculations can screen tens of thousands of materials to identify top candidates for further examination. This type of screening can also be used to investigate trends between structure and function, which can be used to facilitate the design of new materials for specific applications.

My graduate research focused on using density functional theory to predict vibrational spectra for Si and Ge nanoparticles with impurities and heterojunctions. By understanding how changes to the geometry and electronic structure affect the Raman response, we can interpret experimental spectroscopic data to infer the size, dopant concentration, and interfacial strain in these nanoparticles. The calculation of vibrational spectra from first principles for a system containing hundreds of atoms is computationally expensive; therefore, we also introduced an efficient high-order integration scheme for calculating interatomic forces. This method provides good accuracy at a substantially lower computational cost and reduces memory requirements.

My postdoctoral work involves using high-throughput screening methods to discover new nanoporous materials for hydrogen storage, concentrating specifically on metal-organic frameworks (MOFs). MOFs are porous materials consisting of metal nodes connected by organic linkers. They are appealing candidates for gas storage and separations due to their porosity and high surface area. Due to the diversity of available nodes, linkers, and functional groups that can be combined to create a MOF, there are practically an infinite number of hypothetical MOFS. Using high-throughput computational methods to generate new structures and predict their textural properties and capacity for hydrogen storage, we have sifted through huge numbers of MOFs to identify those which are most promising and should be studied in more detail. We also identified structure-property trends and design principles for what an “ideal” MOF for hydrogen storage should look like, which can inform the creation of new MOFs tailored to a specific function.

In the future, I plan to build on my background in molecular modeling, materials science, and high-performance computing to discover new materials with applications in efficient chemical separations and energy production and storage. I am also interested in studying the behavior of MOFs at interfaces with other materials, such as metal nanoparticles or graphite oxide.

Teaching Interests:

My goal as an instructor in chemical engineering is to give students necessary foundational knowledge, while also imparting a sense of the broader connections between various courses they will take throughout their education. Therefore, I am interested in teaching any of the fundamental courses such as thermodynamics, kinetics, transport, and mass and energy balances. Since my background is primarily in molecular modeling and computation, I would also be interested in teaching courses such as numerical methods or MATLAB programming. I am also intrigued by the opportunity to teach more applied courses such as chemical engineering design, which draw from—and synthesize—all elements of chemical engineering.

In addition to these courses, I would be interested in designing and teaching an elective course for graduate students taken from my own research, which focuses on computational materials science and molecular modeling. The course would offer an introduction to density functional theory, molecular dynamics, Monte Carlo methods, and high performance computing. This would be a valuable primer for graduate students who plan to pursue computational research and also provide an accessible overview for students in experimental research who want to gain a sense of how modeling is done and what advantages and limitations various techniques offer.