Recent & Continuing Research Interest Summary:

Within my more recent studies, I apply Large Language Models (LLMs), Artificial Intelligence (AI) and advanced software programming techniques to solve complex challenges in chemical, process, and engineering.

 One of the ways that I achieve this goal is to automate simulation input generation and preparation for large-scale chemical and physical systems.

I then use LLMs trained in data science, plotting, database organization and associated analytical tools to look for patterns in the resulting simulation data and to recommend the optimum conditions for chemical and engineering outcomes.

Then, I apply the wiser, more learned LLM to suggest additional variables to modify in terms of molecular structure and / or desirability of physical constants for such structures in a theoretical material design format to iterate and provide further improvements to the design of the systems.

I am currently applying these methods to two key, broad fields: broad visible spectrum OLED efficiency / out-coupling enhancement & surfactant molecular design.

Since my MS Thesis, I have explored full-scale pixel simulations of OLEDs and applied transfer matrix methods in non-scattering enhanced OLED stacks to determine the relative strength of the microcavity effect.

I have become increasingly interested in the relationship between intermolecular forces and their macroscopic properties, particularly in polymeric materials via computational molecular dynamics.


Organic Light Emitting Diodes & Optimization of Out-coupling Efficiency via Nanoparticulate Scattering Enhancement:

Where I have previously worked on increasing the efficiency of the OLED device in my master's thesis, more recent and yet unpublished studies have calculated the effects each of the optical phenomena on limiting the efficiency of the device from first principles.  

After reporting these achievements to the broader academic and industrial communities, my future goals are six-fold: