(6cc) First-Principles Approaches for Catalyst Design: Novel Descriptors and Strategies for Materials Discovery

Authors: 
Gauthier, J., Technical University of Denmark
Background:

Catalytically driven chemical processes are ubiquitous in a variety of chemical industries and renewable energy technologies. The rational design of catalysts to optimize for activity, selectivity, stability, and cost has the potential to dramatically improve many of these processes. In the past two decades, the ability to understand and design catalyst materials in silico using computational electronic structure theory has revolutionized the field of catalysis. Descriptor based approaches to catalyst design have successfully been used to both understand experimental trends, and to design and discover improved catalysts. In one example, the appropriate descriptor for electrochemical reaction energetics is identified to be not the work function, but the total surface charge, solving a standing problem in the field and allowing for investigation of alkaline conditions for the first time. In a second example, a machine learning approach was used to identify important descriptors for dissociation reactions of diatomic gas phase molecules on metal surfaces.

Research Interests:

A critical challenge in computer guided understanding and design of catalysts is the identification of correct descriptors for activity. My research focuses on understanding catalytic processes through the use of first-principles (ab initio) electronic structure theory in combination with microkinetic modeling. From these insights, I will identify new descriptors for surface reactivity, particularly for electrochemistry where fascinating new behaviors have been observed that cannot be explained by the traditional d-band model.

Due to the ever increasing availability and power of supercomputing resources, over the past decade there has been an explosion of data in the space of computational materials science. By leveraging the rapid growth of materials databases, I will use a machine learning approach not only to identify new materials, but to bring new physical insights to surface reactivity descriptors. These descriptors for activity can be leveraged to screen for catalysts and design interfaces in a rational bottom-up approach.

My future work will therefore have three primary directions:

  1. leveraging understanding of electronic structure theory to identify new descriptors for catalyst activity, especially for electrocatalytic processes
  2. machine learning approaches for identification of not only new materials, but new physics
  3. application of identified descriptors to screen catalysts for activity to a variety of reactions relevant for sustainable energy storage/conversion and chemical synthesis.

Teaching Interests:

I am excited to teach and develop courses at both the undergraduate and graduate level. I have found my experiences teaching to be a gratifying experience on many levels. With my background and teaching experience, I would be confident teaching any fundamental chemical engineering course. However I have a particular interest in teaching courses that are synergistic with my research field and expertise. In a course such as thermodynamics, kinetics, or mass and energy balances, I would easily be able to relate class concepts to real world applications.

I have a background in curriculum development as a graduate student, and I would especially be interested in leveraging this experience to develop a course titled Fundamentals of Heterogeneous Catalysis. This course would be designed to be offered to upper level undergraduate students and first or second year graduate students, but could be easily modified to be an upper division graduate level course. Ultimately, my goal and commitment when teaching and mentoring is to bring students to a point where they have confidence in their understanding and abilities that will serve them throughout their life.