UC Davis

Exploratory chemistry is an important branch of computational chemistry where precursor molecules are simulated in reaction-prone conditions without pre-supposed hypotheses. These molecular dynamics simulations can yield many unexpected reaction pathways, confirming existing mechanisms or suggesting new ones. Decreasing cost of hardware and increasing savviness of software make it enticing to explore reaction spaces in this un-guided manner. However, reactions are still rare events buried in data where the molecules spend most of their time not clearing reaction barriers. Therefore, advanced data processing techniques that can parse the simulations for reaction events with high accuracy are valuable ina field with ever increasing data generation.

In this work, a method is described in Chapter 2 which addresses the problem of extracting valuable reaction location data from exploratory simulations with high accuracy and low cost. The method uses first derivatives on bond order time series obtained from Ab Initio molecular dynamics data to predict temporal reaction locations with high accuracy and speed. The two tunable parameters (low pass filtering cutoff and threshold placement on the derivative) are parameterized against a “gold standard” approach that represents the ideal data processing scenario given infinite time and computing resources. This reaction event classifier is showcased on two single-molecule reactive test systems. The first, simpler test case being a heptanylium alkyl carbocation, C7H15+. The other more complicated system is Fe3(CO)9, which is a highly unsaturated iron carbonyl cluster. The effectiveness of the reaction event classifier is analyzed for both systems using heat maps and other custom plots and metrics.

Deep Eutectic Solvents (DESs) are a relatively new type of mixture which exhibits similar properties to ionic liquids, but with lower cost and environmental impact. Until recently, most force fields describing DESs were not polarizable and therefore lacked certain behaviors created by their extremely partially-charged environment. This second project began with the goal of creating a polarizable force field for DESs, but eventually morphed into creating a program to automate the Tinker polarizable force field initialization process with Python. The test molecule for refining the initialization process is urea. Urea is a hydrogen bond donor which when paired with choline chloride in the correct whole-number ratio create a DES. A semi-automated Python method which creates polarizable Tinker force fields with simple setup is explained.