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Together, these studies underscore the transformative impact of HT screening in advancing optoelectronic
materials research. By refining and optimizing materials at multiple stages of the design process, HT
methods enable researchers to systematically explore electronic and structural parameters, thus providing a
pathway for accelerated innovation in PV, photocatalytic, and solar cell applications. HT screening enriches
the material landscape for optoelectronics and supports the development of scalable, efficient solutions for
sustainable energy technologies.

Predicting optoelectronic properties
Accurate prediction of optoelectronic properties is crucial for advancing materials used in solar cells, light-
emitting devices, photocatalytic materials, and photodetectors. Leveraging HT computational screening and
ML has expanded the ability of researchers to evaluate materials across vast chemical and structural
landscapes [124,125] . This section explores recent advancements in ML-enabled predictive models focusing on
crucial properties such as band gap tuning, band alignment, and charge carrier dynamics.

To enhance the understanding of impurity effects on conductivity, Mannodi-Kanakkithodi et al. developed
an ML approach to predict impurity energy levels in Cd-based chalcogenides, a crucial factor for controlling
[126]
conductivity in optoelectronic applications . As shown in Figure 5A, they used a DFT dataset to train
regression models to predict impurity formation enthalpies and charge transition levels across different Cd-
based compounds. This approach allowed for high-accuracy predictions of impurity effects in CdS, CdSe,
and CdTe, enabling quick screening of impurity atoms that affect the Fermi level and conductivity type. The
model successfully generalized various mixed anion compositions, demonstrating its power in guiding
material design for tailored optoelectronic properties. Building on this concept of material property
optimization, Wang et al. tackled the challenge of consistent and high-accuracy band gap prediction in
[127]
perovskites, a class with extensive compositional diversity . They developed a robust band gap predictor
through a ML model trained on a rigorously compiled dataset of band gaps verified by quasi-particle self-
consistent GW calculations. Figure 5B provides a detailed depiction of the accuracy of the ML model in
predicting the band gaps of perovskites. The figure compares the model predicted band gap with the
reference DSH band gap values, evaluating the accuracy and robustness of the model in both the training
and testing datasets. The model demonstrates low deviation in both datasets, characterized by MSE and an
R value exceeding 98%, indicating high predictive accuracy and generalizability across various perovskite
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compositions. Furthermore, the model effectively predicts band gap in single perovskite structures and
shows excellent accuracy in double perovskite structures. This model demonstrated high transferability
across 15,659 single and double perovskite compositions, identifying 14 lead-free perovskites with band
gaps in the ideal range for PV applications, including MASnBr and FA InBiBr . These materials exhibit
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direct band gaps, low effective masses, and minimal exciton binding energies, improving charge separation
and light absorption efficiencies. Thus, the model broadens the perovskite material space and provides a
reliable tool for discovering efficient, non-toxic PV materials.

Further extending the predictive capabilities of ML, Kim et al. explored the use of B-site alloying in metal
halide perovskites (MHPs), focusing on their stability and band gap properties ; the schematic workflow
[128]
of the study is shown in Figure 5C, given the structural and chemical instability of lead-based perovskites,
employed CNNs to analyze 41,400 B-site-alloyed MHP configurations, simulating each with DFT and
estimating stability based on decomposition energy and band gap type. The validation results of the trained
crystal graph convolutional neural networks (CGCNN) model are also shown in Figure 5C. The parity plots
show the relationship between the decomposition energy (ΔH decomp ) and bandgap predictions predicted by
CGCNN and the DFT calculated values, with very high MAE and R values, indicating very high prediction
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accuracy. Also shown are the confusion matrix and key classification metrics (accuracy = 0.96, precision =
0.84, recall = 0.90, and F1 score = 0.87), validating the model performance in distinguishing between indirect