RUI: CMMT: Computational Study of Ternary Metal Halides for Optoelectronics: Structural, Electrical and Defect Properties

Project: Research project

Project Details

Description

NONTECHNICAL SUMMARY

This award supports theoretical and computational research on ternary metal halide semiconductors. Ternary metal halides are an emerging class of materials including two heavy metals (e.g. silver and bismuth) and halide atoms (e.g. iodine). This project focuses on a set of complex crystalline materials in this family which are flexible and cheap; in addition, they are good at converting light into electricity and are being incorporated in solar cells, sensors and wearable electronics. Despite promising initial experimental results, ternary metal halides are new and many of their properties are not well established or understood. Specifically, it is unclear how structure and defects affect their performance in electrical devices. In this project, the PI and his team will use computational materials modeling and data science methods to develop an atomic level understanding of the structural, electrical, and defect properties of ternary metal halides and to discover new materials in this family.

This awards also supports various educational and outreach activities. The research will involve training of undergraduate students in computational materials modeling and data science methods. The students will visit collaborating research institutions to gain additional training and experience. The results from this project will be placed in the context of related work on solar cells and renewable energy, and presented in a workshop accessible to a wide audience, including high school teachers, students of all ages, and the general public. Finally, new college-level curriculum on machine learning and materials science will be created.

TECHNICAL SUMMARY

This award supports theoretical and computational research on ternary metal halide ionic semiconductors using a combination of density functional calculations and data-science methods to (i) identify the atomic-level defect environments in the Ag-Bi-I system that are responsible for bottlenecks in their optoelectronic device performance, and (ii) discover new ternary metal halide semiconductors by creating a database of microscopic-level calculations for hundreds of new systems.

Recent experimental studies have produced a wealth of information regarding ternary metal halides and the optoelectronic devices incorporating them. Specifically, new solar cells employ various Ag-Bi-I compounds, which are part of the Rudorffite class of complex semiconductors. The PI will generate new microscopic models of Ag-Bi-I that will match the experimentally determined range of stoichiometries. New random forest modeling simulations will be employed to explore the various arrangements of cations, which can run into the billions. Using the generated microscopic models, the PI will calculate basic properties useful for modeling electrical device performance such as band gaps and effective masses. In addition, the PI will explore candidate bulk point defects and dopants, characterize their properties, and compare to available experiments. The results will be tabulated into a searchable database. High throughput and machine learning methods will be developed to accelerate the generation of accurate results.

This awards also supports various educational and outreach activities. The research will involve training of undergraduate students in computational materials modeling and data science methods. The students will visit collaborating research institutions to gain additional training and experience. The results from this project will be placed in the context of related work on solar cells and renewable energy, and presented in a workshop accessible to a wide audience, including high school teachers, students of all ages, and the general public. Finally, new college-level curriculum on machine learning and materials science will be created.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

StatusActive
Effective start/end date9/1/218/31/24

Funding

  • National Science Foundation: $60,354.00

Fingerprint

Explore the research topics touched on by this project. These labels are generated based on the underlying awards/grants. Together they form a unique fingerprint.