(4ha) Theory-Guided Transformations of Inorganic Materials for Sustainable Energy Conversion and Storage

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 conversion 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 account for the ability to realize that material in a laboratory (synthesizability) or how the material will perform when exposed to the conditions of a given device. In this poster, I will discuss how computational approaches to materials design can be dramatically improved by predicting the transformations that materials will and will not undergo. The foundation of my group will be developing theories and methods to understand how materials interconvert between one another during synthesis and during operation in devices. To address this grand challenge, we will leverage expertise in three core areas – applied thermodynamics, solid-state chemistry, and machine learning. These three areas form the pillars of my prior work, which has sought to understand the mechanisms that stabilize inorganic materials using thermochemical calculations (e.g., Nature Communications 9 (1) 4168), chemical bonding principles (e.g., Nature Materials 18 (7), 732), and data-driven descriptors (e.g., Science Advances 5 (2), eaav0693). Moving forward, my group will continue to make advancements in these fields as we push the materials design paradigm forward with predictive theories for solid-state reactivity.

Teaching interests

My interest in pursuing an academic career was instigated by my experiences as a mentor of undergraduate researchers and cemented by my experiences teaching lectures during my Ph.D. at CU-Boulder and now as a postdoc at UC-Berkeley. My graduate experience at CU, and in particular teaching alongside Professors John Falconer and Will Medlin, provided unique exposure to the Chemical Engineering education. Prof. Falconer has been a pioneer in advancing the Chemical Engineering education, encouraging the use of flipped classrooms, active learning, interactive simulations, and screencasts. In his paper on active learning in AIChE Journal, he likens the preference for lecturing instead of active learning to teaching someone how to ski by talking for an hour about how to ski. It is imperative that students not only be taught concepts but have their understanding tested during supervised in-class problem solving. The data is extremely clear – active learning and flipped classrooms improves student outcomes and that improvement is proportional to the extent with which these two concepts are adopted. Having obtained my B.S. and Ph.D. in Chemical Engineering, I’m comfortable teaching and incorporating active learning in any core course. I also plan to develop electives in two areas – data science with Python and nanoscale energy transport in solids – where each course will build upon the foundations of the Chemical Engineering curriculum, making connections to thermodynamics, kinetics, and transport as we discuss real-world applications of these principles in each area.