(6db) Predictive Descriptors for the Targeted Synthesis of Solid-State Materials

Paramount to meeting the increased demand for renewable and efficient energy sources is the discovery and design of next-generation solid-state materials, which are the active components of countless energy storage and generation devices including batteries, fuel cells, photovoltaics, and heterogeneous catalysts. First-principles electronic structure calculations utilizing density functional theory (DFT) have emerged as a standard tool in the computational characterization of materials and can be used in a high-throughput manner to reliably predict the thermodynamic stability of new materials with targeted properties. However, thermodynamic stability alone does not determine synthesizability and does little to indicate how the material of interest should be synthesized—e.g., what precursors, temperatures, pressures, atmospheres, etc. lead to this material being realized in the laboratory. This challenge is made especially daunting by the vast diversity of chemistries, compositions, and structures that can be realized in the solid state. My research group will approach this challenge by a combined theoretical and data-driven approach. High-throughput DFT calculations allow for the rapid generation of thermodynamic and kinetic data for bulk structures and interfaces. By parsing these calculations with the help of sophisticated statistical approaches, we will pursue so-called descriptors that enable us to relate geometric, thermodynamic, and electronic signatures in a specified target or precursor material to the most likely synthesis pathway that these materials will undergo. Ultimately, our work will accelerate the realization of new technologically relevant materials by providing fundamental understanding as to how synthesis proceeds in the solid-state and predictive tools that rapidly convey that understanding through accurate high-throughput predictions.

Teaching Interests:

My desire to pursue a tenure-track faculty position was cemented in my third year of graduate school when I worked as a teaching assistant for graduate Analytical Methods in Chemical Engineering and began mentoring undergraduate research assistants. I’ve found many parallels between teaching and mentoring students, especially the critical nature of drawing clear connections between the material that is being presented and how that material manifests in “real world” applications that are tangible to a Chemical Engineer. Performing fundamental research in the area of solid-state synthesis uniquely positions me to interact directly with each of the pillars of Chemical Engineering – thermodynamics (e.g., phase diagrams), kinetics (e.g., ion diffusion), and transport (e.g., within a reactive particle). My expertise in data science, quantum mechanics, and solid-state chemistry also presents an opportunity for me to develop courses that are typically outside the norm for a Chemical Engineering curriculum but have significant utility in exposing undergraduate or graduate students to disciplines that are relevant to both industrial and academic careers.