As a postdoctoral research scholar at Los Alamos National Laboratory, I devised a novel method utilizing ab-initio simulations to compute the redox potential of actinide chlorides. This approach demonstrated a remarkable computational efficiency enhancement of approximately 100 times (depending on the system) with potential for significant accuracy improvement. Additionally, I was actively involved in developing an accelerated model for simulating ionic systems by integrating Density Functional Tight Binding (DFTB) with machine learning techniques. This innovative approach holds promise for further optimizing the efficiency of redox potential calculations.

As a postdoctoral researcher, I conducted computational studies on the structure and dynamics of high-temperature molten salts. We developed a polarizable-ion model capable of predicting molten salt properties across various temperatures and concentrations. This model, furthermore, significantly enhanced the computational efficiency of polarizable ion simulations by 30 times while maintaining accuracy. My investigations extended to analyzing the interfacial structure of high-temperature molten salts through electrostatic potential and x-ray reflectivity computations. Additionally, we proposed a computational approach to assess the impact of impurities on molten salt structure, which garnered recognition in C&EN. Throughout, I collaborated with a diverse team, leveraging both experimental and computational methods to address research challenges.