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Figure 10B details the effect of formation energy on stability predictions, showing the prediction results with
formation energy as a descriptor, demonstrating how it improves the model accuracy in predicting the
stability of various Haeckelite configurations. And the distribution of the 13 Haeckelite compounds is
identified as stable within the output space, while providing a comparison with regression results when
formation energy is excluded as a descriptor. Figure 10C presents the predicted bandgap values for the
screened Haeckelite compounds, highlighting those within an ideal range for optoelectronic applications.
Additionally, it displays a heatmap generated using Shapley Additive exPlanations (SHAP) values, which
assesses the relative impact of each feature on the bandgap prediction. Key descriptors, including d-electron
fraction and atomic radius, proved influential, confirming the robustness of the RFR model in selecting
Haeckelite structures with desirable electronic properties. As a result, the study identified 13 Haeckelite
structures with excellent stability and suitable bandgaps, which are highly compatible with optoelectronic
applications. These structures exhibit phase stability and promising electron mobilities and absorption
coefficients, making them strong candidates for optoelectronic devices where efficient light absorption and
charge transport are crucial.

Further expanding on structural diversity, Li et al. focused on hybrid heterostructure semiconductors
(HHSs) by combining ML and HT screening to identify hybrid organic-inorganic semiconductor
[148]
superlattices with desirable optoelectronic properties . Targeting organic-inorganic semiconductor
superlattices, the model screened over 200 structural variants to analyze their thermodynamic stability,
electronic structures, and optoelectronic characteristics. Through this approach, 96 HHS candidates were
identified with stable band gaps and efficient carrier mobility, attributes ideal for PV applications. As
depicted in Figure 11A and B, hybrid organic-inorganic semiconductors are structurally classified into Type
I (hybrid ion-substituting semiconductors) and Type II (hybrid heterostructured semiconductors). The
model predictions, comparing the formation energies (E ) of these structures, demonstrated high
form
prediction accuracy, providing a strong framework for material discovery and optimization in advanced
optoelectronic applications. Meanwhile, Chen et al. examined double hybrid organic-inorganic perovskites
[149]
(DHOIPs) by integrating ML and HT screening to identify candidates for solar energy applications . By
integrating ML with HT screening, the study assessed a vast pool of 78,400 DHOIP candidates based on
critical criteria: charge neutrality, stability, non-toxicity, and bandgap suitability. This ML-driven
framework is depicted in Figure 11C, which outlines the sequential screening and refinement process used
to filter down the candidate pool, enabling the systematic identification of perovskites with desirable
bandgap ranges tailored for solar cells. The ML model employed in this study incorporated specific
structural features, notably the anisotropic shapes of organic cations, which significantly enhanced the
prediction accuracy for optoelectronic properties. As a result, the model was capable of accurately screening
perovskite structures that balanced electronic properties with physical stability. Ultimately, the approach
narrowed the initial candidates to a promising list of 19 DHOIPs. These shortlisted compounds were further
validated through DFT, which confirmed their stability and suitability as PV materials.

To develop more efficient, stable, and sustainable next-generation optoelectronic materials, Chen et al.
developed a comprehensive data-driven platform to support the discovery and exploration of 2D HOIPs .
[150]
Figure 12A illustrates the detailed ML workflow, encompassing stages from data collection and feature
engineering to model selection, training, and final prediction. This structured ML pipeline enables accurate
assessments of structure-property relationships by incorporating relevant descriptors that expand beyond
conventional classifications. Figure 12B showcases the platform’s extensive database, structured around
geometric descriptors and an ML model, offering a powerful 2D HOIPs exploration tool. This platform
addresses limitations in traditional classification methods for structure-property relationships by integrating
structural descriptors, known as structure factors (SFs), into the ML framework. This platform integrates