Samuel Odoh: Computation screening of stable materials for photocatalytic water splitting
Title
Computation screening of stable materials for photocatalytic water splitting
Mentor
Department
Biosketch
My research interests are in theoretical/computational chemistry. My group is focused on the development of quantum-chemical methods for treating strongly correlated solid-state materials and for modeling the electronic structure properties of large systems. The aim is to use these methods to guide the design of complex systems with potential impact in catalysis and energy generation.
Project overview
Hydrogen gas is interesting as a green fuel as it produces only water when combusted. However, current technologies for hydrogen gas generation are either inefficient or environmentally “dirty.” For this reason, scientists are racing to develop materials, called photocatalysts, which use light energy to generate hydrogen gas from water. Unfortunately, many known photocatalysts are either inefficient or they degrade rapidly with time. We are interested in a set of compounds called aza-triangulenes. These molecules are completely organic, and have some fundamental properties that allow them to degrade at a much slower rate than conventional photocatalysts. More positively, we can “technically” adjust the properties of aza-triangulenes to increase their performance as photocatalysts for water splitting. The only problem is that only very few of these aza-triangulenes are currently known.
We are computational chemists, and we use quantum mechanical calculations to investigate molecules that are important in the energy sector. The PREP student in the Odoh group will tackle a certain problem relating to predicting the properties of the aza-triangulenes. To screen the molecular space afforded by aza-triangulenes for the best photocatalysts, we need very cheap calculations (that do not take a lot of time or hardware). So, we seek approaches that are fast (and in most cases yield “poorer quality data”) but that can be “translated” onto higher-quality data. Specifically, how can we correctly estimate higher-quality properties (adiabatic excitation energies and gaps) using results from lower-quality calculations (vertical excitation energies and gaps). The student will develop life-long skills like experience with UNIX systems, python coding, computational chemistry software as well as the ability to analyze large sets of data, in addition to the exciting scientific discoveries.
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