Simulating and Designing Circularly Polarized Luminescence with Quantum Dynamics and Machine Learning

Guido et al. (2025) point to the growing importance of CPL for optoelectronics and molecular design, yet detailed simulations of CPL, especially under dynamic (non-equilibrium) conditions, are rare. This idea proposes to use quantum dynamics (e.g., on-the-fly nonadiabatic simulations, as in Restaino et al. 2025) to model CPL spectral evolution, including vibronic and solvent effects, for chiral organic and organometallic molecules. Layered on top, machine learning models trained on these simulations (and available experimental data) would suggest new molecular scaffolds or substituents predicted to maximize CPL intensity and selectivity. The novelty is in integrating high-fidelity dynamical simulations with ML-driven inverse design for a property (CPL) that is both technologically relevant and theoretically challenging. This could accelerate the discovery of next-generation CPL materials for displays, sensors, and quantum technologies.

References:

1. Quantum Chemistry Calculations of Circularly Polarized Luminescence (CPL): From Spectral Modeling to Molecular Design.. Ciro Achille Guido, Francesco Zinna, G. Pescitelli (2025). Chemical Reviews.
2. Simulation of time-resolved site-selective X-ray spectroscopy tracing nonadiabatic dynamics in meta-methylbenzophenone.. Lorenzo Restaino, T. Schnappinger, Markus Kowalewski (2025). Physical Chemistry, Chemical Physics - PCCP.