Reconstruction of Nuclear Ensemble Approach Electronic Spectra Using Probabilistic Machine Learning.

ABSTRACT: The theoretical prediction of molecular electronic spectra by means of quantum mechanical (QM) computations is fundamental to gain a deep insight into many photophysical and photochemical processes. A computational strategy that is attracting significant attention is the so-called Nuclear Ensemble Approach (NEA), that relies on generating a representative ensemble of nuclear geometries around the equilibrium structure and computing the vertical excitation energies (ΔE) and oscillator strengths (f) and phenomenologically broadening each transition with a line-shaped function with empirical full-width δ. Frequently, the choice of δ is carried out by visually finding the trade-off between artificial vibronic features (small δ) and over-smoothing of electronic signatures (

SUBMITTER: Cerdan L 

PROVIDER: S-EPMC9097286 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature