Computational Discovery of Materials for CO2 Capture

Project Description:

There has been a rapid increase in atmospheric CO2 levels over the past century, sparking significant concerns about global warming. Metal-organic frameworks (MOFs) are a promising class of “spongy” porous materials for capturing CO2 from the atmosphere or point emission sources. Their modular synthesis nature allows for targeted tuning of material properties and for virtually unlimited variations in the material’s structure and chemistry.

We are looking for a highly self-driven undergraduate student at UB to test a new molecular simulation method for modeling gas adsorption in MOFs. The student will play a major role in testing this new simulation method on characteristic MOF materials and evaluating the computational efficiency and accuracy of the method. The student will also have the chance to dive into the development of new simulation algorithms. The ultimate goal is to apply this new molecular simulation method to efficiently search the large materials space for promising MOFs that can be eventually deployed in practical chemical processes for CO2 mitigation. This is a chance to learn how to simulate molecular behavior in silico and how machine learning can help solve global challenges. Join our team and let’s discover new materials to save the planet!

Project Outcomes:

The student will gain valuable skills and knowledge in molecular modeling, machine learning, and computational materials, in the context of gas adsorption in nanoporous materials. They will also develop strong communication skills through presentations in group meetings and one-on-one meetings with the advisor. If the project is successful, the student will have the chance to publish their research outcome in professional journals and present their work at local symposiums or national conferences.

Other Information:

Financial support: 1 position @ $9,750
Length of commitment: About a year (12 months). Longer commitment is preferred.
Start time: Anytime
Location: Hybrid (in-person or remote)

If you are interested in this position, please email kaihangs@buffalo.edu with your interests, CV, and transcript.