Abstract Title
Exploring LaCoO3's Catalytic Efficiency for Water Splitting Through Computational Methods
Additional Funding Sources
ACS Project SEED, NSF (Title: Multiscale and Machine Learning Approaches for Electrified Interfaces | Award Number: 2306929)
Abstract
Water splitting, a promising approach for renewable energy production, relies on efficient catalysts to drive the oxygen and hydrogen evolution reactions. In this study, we investigated the potential of the perovskite oxide LaCoO3 as an effective water splitting catalyst by analyzing its catalytic properties using a combination of density functional theory (DFT) calculations and machine learning techniques. The LaCoO3 catalyst was modeled as a slab and subjected to DFT calculations in a 8 by 8 grid of multiple adsorbates. The calculations revealed the different adsorption energies involved in the water splitting process, providing valuable insights into the interactions between LaCoO3 and water molecules as well as a measurement of its overall catalytic efficiency. To speed up the computational process, machine learning algorithms were utilized to predict DFT calculations without the need for time-consuming calculations. By training the model on our dataset of precomputed DFT results, we developed a predictive model capable of estimating the adsorption energies for LaCoO3 and other similar perovskites.
Exploring LaCoO3's Catalytic Efficiency for Water Splitting Through Computational Methods
Water splitting, a promising approach for renewable energy production, relies on efficient catalysts to drive the oxygen and hydrogen evolution reactions. In this study, we investigated the potential of the perovskite oxide LaCoO3 as an effective water splitting catalyst by analyzing its catalytic properties using a combination of density functional theory (DFT) calculations and machine learning techniques. The LaCoO3 catalyst was modeled as a slab and subjected to DFT calculations in a 8 by 8 grid of multiple adsorbates. The calculations revealed the different adsorption energies involved in the water splitting process, providing valuable insights into the interactions between LaCoO3 and water molecules as well as a measurement of its overall catalytic efficiency. To speed up the computational process, machine learning algorithms were utilized to predict DFT calculations without the need for time-consuming calculations. By training the model on our dataset of precomputed DFT results, we developed a predictive model capable of estimating the adsorption energies for LaCoO3 and other similar perovskites.