We develop and apply computational tools (first-principles methods and machine learning models) to aid the discovery of new materials (heterogeneous catalysts, single atom catalysts, nanoparticles, and polymers) for sustainable energy applications (electrochemical CO2 reduction, water splitting, solar cell, and battery).

Computational design of single atom catalysts for electrochemical CO2 reduction


The electrochemical CO2 reduction (CO2R) offers dual promise of recycling significant quantities of the most prevalent greenhouse gas while producing valuable fuels and chemical feedstocks without resorting to further petroleum extraction. Nevertheless, no practical electrocatalysts for this reaction exist, impeding its further development and commercialization. Single-atom catalysts (SACs) recently emerged as promising electrocatalysts for CO2R due to their tunable and scalable active sites, coordination environments, and electronic structures. To aid in the discovery of better SACs, understanding CO2R reaction (CO2RR) mechanism on such materials is foundational. Given the lack of atomic-scale, in situ interfacial probes available experimentally to identify reaction intermediates, mechanistic analysis from reliable quantum mechanical simulations can fill in gaps in understanding the chemistry of SACs in catalyzing CO2R. Here, we propose to elucidate CO2RR mechanism on selected SACs using a multifaceted computational strategy and then apply the insights emerged from mechanistic analysis to design efficient SACs with improved activity and selectivity. We will advance quantum mechanical methods by combining density function theory and high-level correlated wavefunction theory to enable qualitatively and quantitatively accurate prediction of reaction mechanisms and kinetics. Finally, we will unearth design principles revealed from mechanistic analysis and optimize novel SACs for experimentalists to ultimately synthesize and test for CO2R catalytic activity. By using these techniques, we envision contributing to the development of efficient CO2R electrocatalysts.

Advancing quantum-mechanical methods for accurate predictions of heterogeneous catalysis


Significant computational effort has been expended to study heterogeneous catalysis, but the accurate prediction of reaction mechanisms and kinetics still present a great challenge. Even a small quantitative error in the predicted kinetics can dramatically change the understanding of reaction rates and catalytic activities, and thus effectively designing heterogeneous catalysts necessitates an accurate prediction of their mechanisms and kinetics. The most widely employed methodologies, namely density functional theory (DFT), to study surface reactions, are plagued by electron self-interaction and delocalization errors, and thus frequently fail to adequately describe physical properties and chemical processes. By contrast, a high-level theory, namely embedded correlated wavefunction (ECW) theory, represents a suitable approach to describe such phenomenon of surface reactions. 


In my group, we use ECW theory to investigate reaction mechanisms and kinetics of electrochemical reactions associated with energy storage and conversion, including water splitting, CO2 reduction reaction, and ammonia synthesis. Revisiting reaction mechanisms and kinetics in challenging catalytic systems via ECW theory will engender comprehensive understanding of their catalytic activities, and thus inspire new strategies for catalyst design. Nevertheless, ECW calculations are not “black boxes” as DFT calculations and are more computationally demanding. In order to benefit the broader heterogeneous catalysis community, we also develop DFT methods with enhanced predictive capabilities validated using the ECW-predicted energetics and barriers. These efforts will pave the way to reconcile DFT against more computationally demanding approaches and thereby retrieve higher level of accuracy with low computational cost.

Deciphering synthesis process and surface chemistry of semiconducting nanoparticles


Colloidal semiconducting quantum dots (QDs) possess unique electronic and optical properties, making them ideal for applications in photovoltaics, light-emitting diodes, and biological imaging. However, practical applications of QDs are limited by the difficulties in obtaining optimally narrow QD size distributions and high photoluminescent quantum yields, as well as their sensitivity to surface chemistry. Experimentalists have devoted many years to the development of QDs that aim to achieve controlled growth and modified surface chemistry in a trial-and-error manner. We leverage graphics processing unit (GPU)-accelerated large-scale quantum mechanical techniques to investigate the growth mechanisms of QDs, specifically cluster assembly, core/shell formation, and post-synthetic ligand exchange reaction, and further understand the role of surface chemistry in tuning physical and electronic properties of QDs. The study will guide conventional synthetic schemes toward controlled growth of QDs and shed light on perturbing physical properties of QDs through modification of surface chemistry. The introduced methodology will be generalizable to understand nanoscale materials synthesis that is extremely challenging to understand with conventional techniques, and grant design of new QDs tailored for specific applications. 

Predicting Electrochemical Stability Window of Polymer Electrolytes with First-Principles Approaches

Polymer electrolytes in lithium-ion batteries represent an emerging paradigm for energy storage by offering numerous advantages over liquid or solid electrolytes through enhanced resistance to variations in volume of electrodes during charge/discharge process, safety, flexibility, and processability. Overcoming the current limitations of polymer electrolytes necessitates a fundamental understanding of their electrochemical stability window (ESW), which controls polymer electrolyte performance and degradation during charge/discharge cycles. First-principles simulations can provide valuable insights into such properties. However, the widely used density functional theory (DFT) unfortunately engender foundational errors, resulting in incorrect frontier-orbital and band gaps. Here, we carry out a systematic study to accurately and efficiently predict ESW of selected polymer electrolytes. We first identify the most accurate correlated wavefunction (CW) theory methods in predicting ESW of polymers of which experimental data is available. We then develop DFT approximations with enhanced predictive capabilities validated using the CW-predicted properties. These efforts pave the way to reconcile DFT against more computationally demanding approaches and thereby retrieve higher level of accuracy with low computational cost. Our work also assists in the rational design of novel polymer electrolytes with improved electrochemical and physical properties.