(4cl) First-Principles Design of Materials for Catalysis and Separations

Global energy consumption is expected to duplicate by the end of the century; at the same time, reaching net-zero emissions by 2050 is essential to keep global warming within the 2 ºC limits established by the Paris agreement. Achieving these goals requires decarbonizing fuels/feedstock production and scaling up carbon capture, but discovering materials enabling of this transition is a slow, labor-intensive process. While computational methods based on density functional theory (DFT) and machine learning (ML) can downselect materials candidates prior to experimentation, virtual screenings rarely account for the complex interplay of materials stability, function, and experimental accessibility. This is further exacerbated by the combinatorial number of compositions, structures, and reaction pathways in catalysis and separations, and by the associated computational cost of ab initio simulations. My research group will address three major challenges that hinder materials discovery in chemical engineering:

(i) DFT calculations are overly expensive to explore the chemical space, and conventional ML models often fail to extrapolate towards unseen materials;

(ii) catalyst/separator design relies on elusive relationships between electronic structure and materials activity/selectivity, preventing their inverse design;

(iii) theoretical efforts rarely provide synthesis routes for computer-generated materials.

During my PhD, I approached these problems by combining high-throughput simulations, automated literature extraction, ML, and human-computer interaction, successfully discovering new zeolites and perovskite catalysts. As a Principal Investigator, I will generalize these efforts by working on the following thrusts:

Methods Development: developing physics-based computational models that unify the transferability of DFT and the efficiency of ML for the prediction of electronic structures.

Materials Science: seeking new structure-property-processing relationships in catalysis and separators by connecting high-throughput calculations, literature parsing, and experimental data.

Materials Discovery: applying these tools to develop new thermal, photo- and electrocatalysts for clean fuel production, and porous materials for hydrogen storage and carbon capture.

As such, my group will create fundamental knowledge on materials synthesis and function, develop tools for data-driven materials design, and work closely with industry partners to produce and deploy new technologies to the market. Our vision is to become a center of excellence for materials discovery, accelerate the energy transition, and support the climate demands with cleaner, affordable materials.

Teaching Interests

Training the next generation of thinkers is of utmost importance to address the challenges of our climate crisis. In my career, I worked as a teaching assistant for two semesters in an experimental chemistry lab and one semester in a theoretical physics class, teaching both science and engineering through case studies and projects. Drawing from these experiences and my research background, I am qualified to teach core courses in chemical engineering such as thermodynamics or kinetics, as well as electives in quantum mechanics, solid state physics, or atomistic simulations. In addition, my knowledge in machine learning may enable the development of a multidisciplinary course on Applied Machine Learning, which would teach data science and statistical learning techniques to solve problems in chemical engineering and beyond.

I also mentored several undergraduate and graduate students from diverse backgrounds. Having trained junior investigators for years and created video content ranging from onboarding details to software skills, I am excited to develop workshops on meta-skills for graduate research. By supplementing students’ training with topics rarely covered in their curriculum, including data visualization, applied statistical analysis, version control, and computational thinking, I will provide accessible content to start and refine the “tools of the trade” of their research career.