Authors:

Gavin Bidna

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About Paper:

Halide perovskite (HaP) semiconductors are a promising class of materials for a variety of photo-driven applications, including photocatalytic reduction of pollutants and photocatalytic electrolysis for green hydrogen generation. The optoelectronic and catalytic properties of HaPs are incredibly tunable via composition, structure, and surface engineering. The widely accessible chemical space of HaPs lends itself well to high throughput screening of materials using machine learning, but to be able to predict photocatalytic behavior, the machine learning models require a database of surface and adsorption properties to learn from. Using first principles-based density functional theory (DFT) simulations, our group plans to calculate the surface energies and small molecule adsorption energies at various sites for a variety of different surface terminations and crystallographic orientations for a compound characteristic of the chemical space, CsPbI3. Once the favorable surface termination and adsorption sites are identified, surface and adsorption energies will be calculated for 8 other compounds belonging to the family of compounds Cs(P b, Sn, Ge)(I, Br, Cl)3. From these calculations, we hope to gain preliminary insight in how different surface terminations and compositions affect the surface stability and adsorption behavior. Furthermore, the data generated by these calculations will be vital for training models for high throughput screening of materials for photocatalytic applications.

Source:

Purdue University / 2023

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Co-authors:

Gavin Bidna