Year: 2019
Type: dissertation
Abstract: This thesis investigates new electrocatalysts for the oxygen evolution reaction, which is critical for the feasibility of water electrolysis. The evolution reaction can be used to store renewable energy that is produced intermittently. The discovery of new electrocatalysts is hindered by the vast chemical space. By developing a high-throughput method, I first targeted a group of promising crystal structures and narrowed the searching space for the oxygen evolution reaction using computational screening based on density functional theory. A machine learning algorithm was then implemented based on the database constructed. The 1st ionization energy and the atomic number were found to be accurate descriptors for the adsorption energy of 𝑂𝐻 and 𝑂𝑂𝐻. I used these two parameters as input for the random forest regressor for predicting the adsorption energies on doping materials. This method has thus far identified around 400 doping materials for a single type of crystal structure. The predicted catalyst properties agree with the reported experimental results and accelerate finding the top candidates for experimental validation. I validated the predicted results with reported data to ensure that the models are grounded in practice and consistent with experiments.
Source: TSpace (University of Toronto)
Author: Yimeng Min
Language: English
Field: Energy
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2987188040