(4dg) Bridging Atomistic and Experimental Scales in Electrochemistry for Energy Storage and Catalysis

Electrochemical devices will play a significant role in addressing the global climate crisis by facilitating the transition to renewable energy. For example, batteries can electrify transportation and balance grid-level electricity generation, and electrocatalysts can sequester CO₂ at the point of generation, or directly from the atmosphere to convert it to chemical feedstocks. Addressing the grand challenges of next generation energy storage and catalysis such as ultra-low overpotential anodes, high ionic conductivity solid-state electrolytes, and efficient CO₂ reduction will require the discovery and optimization of new materials with unique, and often rare, properties. I will develop new understanding and accelerate the design of electrochemical systems by leveraging a comprehensive toolset spanning computational (density functional theory, ab initio and classical molecular dynamics) to high throughput experimental techniques, leaning mostly towards computational. My lab will serve as a means of testing atomistic hypotheses for electrochemical kinetics for batteries and catalysis and generate high throughput, closed-feedback, research into electrolyte and electrode materials to create autonomous optimization and learning (Figure 1).

The overarching goal of my research is to develop fundamental insights of electrochemical systems from first principles simulations and automate the design/optimization of new materials through novel generative data science models. To solve the grand challenges of electrochemical materials, my proposed research lies at the intersection of ab initio calculations of electronic properties and data science to enable automated material design. I will leverage first principal simulations with unique generative learning to achieve three research goals:

• Automated discovery of solid-state electrolytes through inverse design.
• Elucidate and optimize the effects of additives on electrochemical interfaces for C₂/C₃ product selectivity from CO₂
• Tailor the atomic structure of high-entropy alloys (HEAs) to achieve high efficiency CO₂

In the near term, my lab will characterize ionic diffusion in diverse solid-state electrolytes (Thrust 1), model the effects of additive chemistries on the electric double layer and CO₂ reduction kinetics (Thrust 2), and design novel HEA active sites for electrocatalysts (Thrust 3). I will simultaneously collect robust high throughput experimental data to validate the atomistic results and develop machine learning models to bridge the two scales. In the long term, the combination of data generation and mechanistic insights through machine learning will provide a platform for autonomous material design and rapid hypothesis testing in electrochemistry and other material science applications.

Ph.D. Research

Chemical and Biomolecular Engineering, University of Houston, Co-advisers: Lars Grabow, Yan Yao

My PhD research focused on developing models of ionic conductivity in solid state electrolytes (SSEs). The multitude of possible transport mechanisms and high computational expense of calculating diffusion coefficients prevents the design and identification of new SSEs. To address these issues, I explored multiple machine learning models to more effectively leverage large detests and extract useful information to design new materials. The representation of the material is critical for capturing physically relevant descriptors, and I design several algorithms based on artificial neural networks and other algorithms to take either crystal structure or electron density as an input and predict both ionic conductivity and probability density. The choice of descriptor and model is key for both performance and interpretability. These novel and multi-faceted computational frameworks for the design of SSEs is transformative and can be easily extended to the design of advanced functional materials with diverse applications.

Postdoctoral Research

SUNCAT, Stanford University, Co-Advisers: Frank Abild-Pedersen, Michal Bajdich

For my postdoctoral research I am studying the activity and stability of photo active electrode materials for the oxygen evolution (OER) and CO₂ reduction reactions. In both cases, the stability and activity of the electrode are inherently correlated to the electron structure of the material, with dissolution and surface reconstruction playing major roles in overall device performance. I've performed detailed density functional theory analysis of the bulk and surface compositions of mixed metal oxides to identify active sites, asses Pourbaix stability and develop mechanisms of photo activity. By expanding conventional micro-kinetic models to include the transport of holes to the surface present under illumination, additional control over the activity and product selectivity for CO₂ reduction is possible. This work will lead to developing new catalysts for OER and CO₂ reduction which enable improved activity and stability under realistic operating conditions.

Teaching Interests

My goal as a teacher is to motivate and inspire student creativity so they become independent critical thinkers to address real world problems. As part of the University of Houston Future Faculty Program, I studied pedagogical practices and gained additional responsibilities regarding teaching of course material. I have teaching experience at both undergraduate and graduate levels, was the teaching assistant for four different classes, and taught guest lectures for Numerical Methods for Chemical Engineers and Advanced Catalysis. I have in-depth knowledge of chemical engineering fundamentals and feel comfortable teaching any course in the core chemical engineering curriculum particularly in thermodynamics, transport, or numerical methods. Moreover, I aim to develop a course in data science and computational methods with applications to electrochemistry.

I’m excited to teach and mentor, and as a teacher I will focus on student centered outcomes and learning to enable future generations of critical thinkers and problem solvers. When mentoring, my goal is to inspire creativity and advanced problem solving and provide students with the tools and resources they can leverage in the future to achieve their own goals.