Group Research

Ammonia (NH₃) is one of the most essential agricultural compounds being used as the primary source of nitrogen in fertilizers. The Haber-Bosch process is a time-tested synthetic method for NH₃ in large quantities from N₂ and H₂. While the reaction of N₂ and H₂ gas is thermodynamically allowed at room temperature and pressure, the initial dissociation of both reactants required to facilitate their combination is highly unfavorable, both thermodynamically and kinetically. Although a transition metal catalyst, such as Fe, may lower the barrier for the dissociation of N₂, high temperature is still required to hasten this process, which consequently demands high pressure to remain nearly spontaneous. Through first-principles methods, I investigate alternative metal catalysts that could not only lower dissociation barrier for N₂ but also harness the energy of light, via plasmon resonance, to facilitate this process. With light instead of heat as an additional driving force, a high-dissociation rate at lower temperatures may be achieved, which would then relax the need for higher pressures and thus improve the energy efficiency of the process overall.

The RPA has attracted rising interest in the recent years as a method on the fifth rung of the Jacob’s ladder. The higher accuracy of the RPA compared to commonly used semi-local density functionals is however accompanied with significantly higher costs, with RPA calculations often exceeding current computational capacity. Using the embedded correlated wavefunction (ECW) theory, we want to accelerate the costly RPA calculations with either a cluster embedding approach or a periodic embedding approach. We also are working on combining the ECW theory with implicit solvation and the grand canonical treatment of electrons, which has been shown to be possible for RPA calculations, to model solvation and electrochemical potential effects.

During my PhD, I was trained in using density functional theory and wave function theory methods to understand the interplay of hydrogen bonds and transition metal complexes in C–H activation, both by enzymes and molecular catalysts. As a member of the Carter research group, I will lead an effort to identify the optimal facets of Cu electrocatalysts for ammonia (NH₃) synthesis by reduction of nitrate (NO₃^–). While Cu electrocatalysts are promising for nitrate reduction to ammonia, the reaction pathway could result in several byproducts which are both pH dependent and electrocatalyst facet-dependent. The standard simulation technique, density functional theory, does not provide the accuracy required to assess electrochemical kinetics. Therefore, I will apply embedded correlated wavefunction theory, a quantum mechanical simulation method developed by Prof. Carter, to simulate nitrate reduction reaction on a variety of Cu facets that could provide a carbon-emission free route to ammonia production.

Our focus is to develop cost-effective strategies for carbon emissions capture and storage by way of a high-efficiency electrocatalytic conversion into hydrocarbons or dissolution in saline water followed by mineralization. We aim to perform a multiphysics modeling of “carbon sequestration” at varying levels of electronic structure theory, invoking different degrees of approximations for regions in the chemical space. This furnishes a workflow to assess accurate reaction pathways while minimizing the computational costs. We employ ab initio molecular dynamics accelerated by rare-event sampling techniques for the wide configuration space, coupled with Embedded Correlated Wavefunction Theory to determine the possible reaction pathways for carbon capture and storage. Here, we invoke Density Functional Embedding Theory, a divide and conquer approach that reduces the complexity of the correlated wavefunction method as applied to the embedded region, with the environment described using a density functional approximation–based embedding potential. Two exquisite applications of this methodology are CO2 electroreduction on [100] and [111] copper surfaces with defects, and carbonate formation from bicarbonates, a critical step in CO2 mineralization in seawater.

Single-atom alloys (SAA) and single-atom catalysts (SAC) combine reactive transition metals with less reactive materials, creating catalysts that are able to defy linear scaling relationships. Combining these architectures with plasmonic metals can improve catalytic performance by enabling nonequilibrium charge and energy transfers. Understanding the ways in which plasmon resonance can alter chemical reactions at these SAAs and SACs is key to further optimizing their performance. I will use capped and standard density functional embedding theory to derive embedding potentials to replace the environments of SACs and SAAS, respectively, which greatly reduces the cost of accurate quantum-based simulations. These embedding potentials will be used within embedded correlated wavefunction theory to understand the thermo and photo-catalytic activity of SACs and SAAs, which will provide insight into how plasmonic resonance energy and charge transfers can alter the excited state kinetics from the ground state kinetics for key elementary chemical reactions at different possible SAA and SAC sites.

Our research aims to develop cost-effective solutions for carbon capture and storage, with a focus on CO2 mineralization in seawater, rich in calcium and magnesium ions, by leveraging machine learning potentials to accurately model and predict the reaction pathways essential for efficient mineralization, allowing us to tackle the complexities of these reactions on a large scale. By integrating multiphysics modeling across varying levels of electronic structure theory, we will identify optimal pathways for the formation of stable carbonates, a key step in CO₂ mineralization. This approach will not only expand our understanding of carbon mineralization in marine environments but will also support the design of scalable carbon sequestration technologies. These efforts contribute significantly to the development of sustainable and efficient methods for reducing atmospheric CO₂.