Equivariant Graph Neural Networks Improve Optical Spectra Prediction for Materials Screening
arXiv:2606.19133v1 Announce Type: cross Abstract: 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
The increasing availability of large materials datasets and advancements in Graph Neural Network architectures are enabling more sophisticated AI applications in materials science.
This development significantly accelerates the materials discovery process for critical optoelectronic applications like solar cells, impacting energy efficiency and technological advancement.
The ability to predict optical spectra more accurately and scalably with AI reduces the need for computationally intensive simulations or extensive lab work in materials screening.
- · Materials scientists
- · Renewable energy sector
- · AI/ML research in materials
- · Optoelectronics industry
- · Traditional high-throughput screening methods
- · Computational chemistry software relying on less efficient models
Faster discovery of new, high-performance materials for solar cells and other optoelectronic devices.
Reduced R&D costs and shortened time-to-market for advanced energy and display technologies.
Acceleration of energy transition via more efficient solar capture and potential for entirely new material functionalities.
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Read at arXiv cs.AI