Electronic Density versus Geometry for Machine-Learned Molecular Absorption Spectra
Molecular optical absorption spectroscopy provides a direct probe of electronic structure and is widely used for molecular identification, interpretation of photophysical behaviour, and planning of spectroscopy experiments. Calculating the absorption spectra using first-principle excited-state methods, however, is computationally demanding, at least compared to ground-state calculations, which limits their routine application across large molecular sets. Machine-learning (ML) surrogates can reduce this cost and allow rapid spectral prediction. However, their performance depends strongly on how molecular information is represented. Here, we compare using the ground-state electron density v...
| Authors | Siddharth Dhanpal, Peter Elliott, Paolo Emilio Trevisanutto et al. |
|---|---|
| Primary category | physics.chem-ph |
| Published | 2 October 2026 |
| Last updated at source | 2 October 2026 |
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