Page 94 - Read Online

Page 18 of 31 Shu et al. J. Mater. Inf. 2025, 5, 36 https://dx.doi.org/10.20517/jmi.2025.13

potentially enhancing the discovery of stable and high-performing materials.

Optimizing optoelectronic performance
The optimization of optoelectronic performance in semiconductor materials is essential for enhancing the
efficiency and applicability of these materials in optoelectronic and related applications. The optimization of
optoelectronic performance can directly influence the energy conversion efficiency, response speed, and
stability of devices, thereby determining the overall performance of optoelectronic components [137,138] . To
address the challenge of pinpointing high-efficiency optoelectronic materials, Cai et al. employed a ML
model combined with HT screening to identify high-performance 2D PV candidates from a database of
187,093 inorganic crystal structures . Through classification-based ML algorithms, their model filtered
[139]
2DPV candidates based on features correlated with high PV conversion efficiency, such as the packing
factor (P), which emerged as a primary indicator of PV potential. Figure 8A categorizes the selected 2DPV
f
candidates according to structural prototypes, illustrating that those materials with specific space groups
(e.g.,P3m1) tend to exhibit favorable PV properties. Moreover, three materials - Sb Se Te, Sb Te , and Bi Se
2
2
2
2
3
3
- were highlighted for their high conversion efficiencies, making them promising candidates for PV
applications. This approach demonstrates the power of ML-assisted HT screening in identifying structurally
promising candidates for further development, showcasing how structure-related features can serve as
indicators for enhanced optoelectronic performance. Building upon this ML optimization approach, Liu
et al. introduced a BO framework with knowledge constraints to optimize the fabrication conditions for
[140]
perovskite solar cells using rapid spray plasma processing (RSPP) . The BO framework incorporated
observations as probabilistic constraints to focus the parameter search on high-quality films. Their method
achieved a 5-round, 18.5% power conversion efficiency with fewer than 100 process conditions through this
iterative optimization approach. Figure 8B outlines this sequential optimization process, mapping the
relationship between process parameters and efficiency improvements across successive rounds.
Additionally, Figure 8C provides a detailed visualization of process conditions and predicted efficiency
correlations, highlighting critical variable interdependencies that inform experimental adjustments, such as
the alignment of spray flow rates and substrate speed. This framework highlights the advantage of
integrating sequential learning with empirical knowledge, allowing researchers to efficiently navigate
complex parameter spaces and optimize fabrication conditions for maximum efficiency gains.

Designing novel optoelectronic materials
The design of novel optoelectronic materials is crucial for the continued advancement of optoelectronic
applications. As the demand for materials with enhanced performance, stability, and environmental
sustainability increases, researchers are turning to innovative approaches to identify and develop novel
materials with optimized properties [4,141] . Recent studies demonstrate that ML and HT screening are
invaluable tools for accelerating material discovery, enabling the efficient exploration of vast chemical
spaces to uncover materials with desirable characteristics for next-generation applications [142,143] . To initiate
the exploration of new optoelectronic materials, Ma et al. employed a HT ML framework to screen for
potential 2D PV materials within the family of octahedral oxyhalides (OOHs) . By training ML algorithms
[144]
on structural and electronic properties from DFT data, the model evaluated a dataset of 5,000 OOH
compounds, ultimately identifying six candidate materials with optimal band gaps and high electron
mobilities suitable for PV applications. Figure 9A visualizes the model workflow, highlighting the multi-
stage screening process and the structural attributes of top-performing candidates, such as Bi Se Br .
2
2
2
Expanding the scope of material discovery, Jin et al. applied ensemble ML techniques to explore the
compositional space of all-inorganic, lead-free perovskites, which address concerns over toxicity and
[145]
stability associated with traditional lead-based perovskites . By developing a physics-inspired
multicomponent neural network, they screened nearly 12 million AA′BB′X X′ compositions. Figure 9B
3
illustrates the band gap prediction accuracy achieved through this ensemble model, showing the close