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<p>Optical spectroscopies provide a powerful means to probe light-matter interactions and complex electronic features that are crucial for the development and optimization of optoelectronic devices, where performance is closely tied to the underlying electronic spectrum. However, realistic modeling of tensor optical responses in materials remains computationally demanding and challenging. Here we introduce the Tensorial Spectra Equivariant Neural Network (TSENN), an equivariant graph neural network architecture that maps crystal structures directly to their full photon-frequency-dependent optical tensors. By encoding isotropic sequential scalar components and anisotropic sequential tensor components into spherical tensor representations, TSENN ensures symmetry-aware predictions consistent with crystalline symmetry constraints. Trained on frequency-dependent dielectric tensors of 1,432 bulk semiconductors, the model achieves a mean absolute error of 0.127, demonstrating its potential for efficient and general modeling of optical properties. Our framework opens new avenues for data-driven design of anisotropic optical responses to accelerate materials discovery for optoelectronic applications.</p>

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Accurate prediction of tensorial spectra using equivariant graph neural network

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月2日

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  • Ting-Wei Hsu,
  • Zhenyao Fang,
  • Arun Bansil,
  • Qimin Yan

摘要

Optical spectroscopies provide a powerful means to probe light-matter interactions and complex electronic features that are crucial for the development and optimization of optoelectronic devices, where performance is closely tied to the underlying electronic spectrum. However, realistic modeling of tensor optical responses in materials remains computationally demanding and challenging. Here we introduce the Tensorial Spectra Equivariant Neural Network (TSENN), an equivariant graph neural network architecture that maps crystal structures directly to their full photon-frequency-dependent optical tensors. By encoding isotropic sequential scalar components and anisotropic sequential tensor components into spherical tensor representations, TSENN ensures symmetry-aware predictions consistent with crystalline symmetry constraints. Trained on frequency-dependent dielectric tensors of 1,432 bulk semiconductors, the model achieves a mean absolute error of 0.127, demonstrating its potential for efficient and general modeling of optical properties. Our framework opens new avenues for data-driven design of anisotropic optical responses to accelerate materials discovery for optoelectronic applications.