Computational Design of Chiral Nanoparticles and their Assemblies with Giant Optical Activity
Abstract
Computer simulations of NPs interacting with other nanoscale structures provide the best approach to describe structural features and specific interactions at the atomic level and higher. The project will include a combination of state-of-art computational methodologies to sample the structures at multiple length scales, mostly by MD simulations and DFT calculations. The analyses will focus on the calculation of chirality measures, such as the Hausdorff chirality measure (HCM, Fig. 4), the Osipov-Pickup-Dunmur (OPD) index, and different types of chiroptical properties (electronic circular dichroism (ECD), vibrational circular dichroism (VCD), terahertz circular dichroism (TCD), and Raman optical activity (ROA)). These different chirality measures provide complementary information about the molecular origins of chiral discrimination. The combination of this information with structural and thermodynamic properties will allow us to create a database to train machine learning (ML) algorithms to predict chirality-driven self-assembly of structures with linear and non-linear optical activity. These structures can be used for near-infrared spectroscopy with polarization capabilities and biosensing of human activity. Once trained and validated, the ML algorithms will allow rapid screening of new NPs for any specific application. The trained ML algorithms will be extensively validated by the group of Prof. Nicholas A. Kotov at the University of Michigan (Ann Arbor, MI).
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Feb 06, 2025
- Source ID
- FA95502410253
Entities
People
- Andre De Moura
Organizations
- Air Force Office of Scientific Research
- United States Air Force