Citing Articles For: Deep learning of spectra: Predicting the dielectric function of semiconductors

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Excitonic effects on the optical spectra and the electron energy loss spectra of Mg2C using linear response theory

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Deep learning of spectra: Predicting the dielectric function of semiconductors

Discovery of Sustainable Energy Materials Via the Machine‐Learned Material Space

Recent advances in the application of Raman spectroscopy in heterogeneous catalysis

qsGW quasiparticle and GW-BSE excitation energies of 133,885 molecules

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Screening for wide-gap, high-refractive-index materials via frequency-dependent dielectric function prediction aided by convolutional autoencoders

A physics-informed graph-ensemble framework for predicting structure-dependent dielectric properties

Many-body perturbation theory vs. density functional theory: a systematic benchmark for band gaps of solids

Accurate prediction of tensorial spectra using equivariant graph neural network

The carbon cost of materials discovery: Can machine learning really accelerate the discovery of new photovoltaics?

AI-Driven expansion and application of the Alexandria database

From electromagnetic field-material interactions to intensified chemical manufacturing via microwave catalysis

Transfer learning of GW Bethe–Salpeter equation excitation energies

Optical spectra prediction using three-body information

Theoretical advances in resonance Raman spectroscopy of solids

Deep Learning–Based Extraction of Promising Material Groups and Common Features from High‐Dimensional Data: A Case of Optical Spectra of Inorganic Crystals

Materials Informatics Framework for Accelerated Discovery of High-Refractive-Index 2D Materials

Screening practical nonlinear optical crystals by using graph neural networks

Machine learning climbs the Jacob’s Ladder of optoelectronic properties

Excitonic effects on the optical spectra and the electron energy loss spectra of Mg2C using linear response theory

Modeling Complex Proton Transport Phenomena─Exploring the Limits of Fine-Tuning and Transferability of Foundational Machine-Learned Force Fields

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