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Figure 11. (A) Schematic representation of organic-inorganic hybrid semiconductors. Hybrid semiconductors can be divided into type I,
HISSs, and type II, HHSs. Among the HOIPs, 2D Ruddlesden-Popper layered perovskite has common features of type I and II; (B)
Tetrahedral bonding HHSs investigated are classified as type II. The GaAs-based HHSs in different phases with different numbers of
inorganic semiconductor sublayers are shown as instances. Copyright 2022, American Chemical Society, Reproduced with
permission [148] ; (C) The framework for screening DHOIPs by combining ML models and DFT calculations. Copyright 2022, Royal Society
of Chemistry, Reproduced with permission [149] . HISSs: Hybrid ion-substituting semiconductors; HHSs: hybrid heterostructure
semiconductors; HOIPs: hybrid organic-inorganic perovskites; 2D: two-dimensional; DHOIPs: double hybrid organic-inorganic
perovskites; ML: machine learning; DFT: density functional theory.

capabilities for searching, downloading, analyzing, and predicting material properties online, providing
valuable resources for further research into HOIPs and other related fields, particularly in energy and PV
applications. The model developed a database of 304,920 HOIP structures through this approach, utilizing
geometric descriptors to precisely predict electronic and structural properties. This innovative data-driven
approach broadens the scope of HOIP materials and establishes a scalable model for discovering materials
with enhanced stability and optoelectronic performance.

To provide a comprehensive overview of the evolution of ML applications in optoelectronic materials
discovery, we have summarized the ML studies discussed above in chronological order, as presented in
Table 1. This timeline illustrates the progression from early implementations utilizing simple descriptors
and regression models to more sophisticated approaches, including CNNs, GNNs, BO, and integrated ML
pipelines. This progress highlights the increasing sophistication and integration of ML methods in the field,
which improves the efficiency and accuracy of the material discovery process. Each study collectively
advances our understanding of ML-driven HT approaches to transform discovery for sustainable, high-
performance optoelectronic materials. Together, they underscore a cohesive strategy: leveraging predictive
models and rigorous validation methods to streamline the development of next-generation materials
essential for future energy solutions.

SUMMARY AND OUTLOOK
We highlight the substantial advancements achieved through HT screening and ML techniques in
discovering and optimizing optoelectronic materials. By integrating computational power with predictive
algorithms, ML has successfully accelerated the identification of candidates with optimized properties for
applications in solar cells, light-emitting devices, photocatalytic materials, and photodetectors, marking a
transformative shift from traditional experimental approaches. HT methods now enable the rapid
assessment of extensive chemical and structural spaces, allowing systematic exploration of parameters that
define key optoelectronic properties. In particular, the application of ML models has enhanced predictive