Neural Network-Driven Atomistic Simulations for Predicting Chiroptical Properties in Ligand Induced Chiral Semiconductor Nanocrystals

About This Grant

Eran Rabani of the University of California, Berkeley, is supported by an award from the Chemical Theory, Models and Computational Methods program in the Division of Chemistry to develop advanced computational models that predict the optical properties of chiral semiconductor nanocrystals. These materials, which are essential components in next-generation technologies such as filters, sensors, and displays can be engineered to exhibit specific properties by controlling their size, shape, and surface chemistry. Rabani and his research group will develop neural network-based atomistic models to understand how these chiral nanocrystals interact with light, overcoming significant challenges in understanding and predicting chiroptical properties at the nanoscale. This work could lead to breakthroughs in the design of the next generation of optical materials, with tailored chiroptical properties. The research will also provide training opportunities for students in theoretical and computational chemistry, and in nanoscience and nanotechnology, fostering the next generation of scientists and engineers. Eran Rabani will develop advanced neural network-based atomistic models to describe the electronic and optical properties of semiconductor nanocrystals with chiral ligands. These models, validated by experiments, will treat materials and molecules at the same theoretical footing, providing insights into the mechanisms that govern the optical activity of chiral nanocrystals. The research will address computational challenges in predicting chiroptical properties by incorporating the effects of ligand orientation, ordering, and thermal fluctuation on the excitonic dynamical properties. Rabani will also investigate chiral biexcitons and their coupling with chiral microcavities, exploring their potential for applications in quantum optics and photonics. The broader impacts include training students in computational chemistry and nanoscience, as well as engaging in outreach activities to inspire K-12 students in STEM fields. 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.