  The frequency-dependent optical spectrum is pivotal for a broad range of
applications, from material characterization to optoelectronics and energy
harvesting. Data-driven surrogate models, trained on density functional theory
(DFT) data, have effectively alleviated the scalability limitations of DFT
while preserving its chemical accuracy, expediting material discovery. However,
prevailing machine learning (ML) efforts often focus on scalar properties such
as the band gap, overlooking the complexities of optical spectra. In this work,
we employ deep graph neural networks (GNNs) to predict the frequency-dependent
complex-valued dielectric function across the infrared, visible, and
ultraviolet spectra directly from crystal structures. We explore multiple
architectures for multi-output spectral representation of the dielectric
function and utilize various multi-fidelity learning strategies, such as
transfer learning and fidelity embedding, to address the challenges associated
with the scarcity of high-fidelity DFT data. Additionally, we model key solar
cell absorption efficiency metrics, demonstrating that learning these
parameters is enhanced when integrated through a learning bias within the
learning of the frequency-dependent absorption coefficient. This study
demonstrates that leveraging multi-output and multi-fidelity ML techniques
enables accurate predictions of optical spectra from crystal structures,
providing a versatile tool for rapidly screening materials for optoelectronics,
optical sensing, and solar energy applications across an extensive frequency
spectrum.
