Equivariant Graph Neural Networks Improve Optical Spectra Prediction for Materials Screening
Scalable prediction of optical spectra is a critical component of high-throughput materials screening for optoelectronic applications such as solar cells. Existing surrogate models are trained on spectra computed from lower levels of theory or rely on rotation-invariant scalar features, limiting their geometric expressiveness. We explore the use of equivariant graph neural networks for optical spectra prediction, adapting GotenNet to this task and evaluating it on multiple datasets including a recently published collection of 10,533 structures with spectra computed at the level of the random phase approximation (RPA). The proposed model outperforms the current state of the art, with the l...
| Authors | Kasper Helverskov Petersen, François R J Cornet, Martin Ovesen et al. |
|---|---|
| Primary category | physics.optics |
| Published | 17 June 2026 |
| Last updated at source | 17 June 2026 |
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