Hybrid Theory-Experiment Feedback Loops for Spectroscopy: Real-Time Correction of Quantum Chemistry Predictions

Fawzy (2023) emphasizes the fruitful interplay between experiment and theory, and Chen et al. (2023) show that machine learning corrections can improve quantum chemistry predictions. This idea envisions a closed-loop system: as spectroscopic data (e.g., ultrafast UV/vis, X-ray, or CPL spectra) is collected, it is immediately compared to quantum chemistry predictions (TD-DFT, EOM-CC, multireference, etc.), and any deviations are used to update physical models or ML-corrected Hamiltonians in near real time. Unlike standard benchmarking or post hoc corrections, this “living” model evolves as data accrues, potentially uncovering new phenomena (unexpected spectral features, temperature/solvent effects) as they arise. The approach is inspired by “self-driving labs” but applied specifically to the excited-state quantum chemistry–spectroscopy interface, promising rapid iteration and discovery, and improved trust in theoretical predictions for new chemical systems.

References:

1. The Interplay Between Experimental Spectroscopy, Theoretical, and
   Computational Quantum Chemistry. Wafaa M. Fawzy (2023). Journal of Chemistry and Interdisciplinary Research.
2. Δ-Machine learning for quantum chemistry prediction of solution-phase molecular properties at the ground and excited states.. Xu Chen, Pinyuan Li, Eugen Hruska, F. Liu (2023). Physical Chemistry, Chemical Physics - PCCP.