Page 92 - Read Online

Page 16 of 31 Shu et al. J. Mater. Inf. 2025, 5, 36 https://dx.doi.org/10.20517/jmi.2025.13

Assessing the stability of optoelectronic materials
The stability of optoelectronic materials is a critical factor for their long-term performance and durability,
particularly in applications such as PV devices and catalysis, where material degradation over time can
significantly affect efficiency and sustainability. Accurately predicting the stability of materials is essential
for designing durable devices and ensuring their practical viability [131,132] . Recent developments in ML have
provided powerful tools for predicting the stability of optoelectronic materials [133,134] . This section reviews
recent advancements in ML-based stability prediction models, highlighting their ability to enhance material
screening and guide the development of stable, high-performance materials. In recent studies, Bartel et al.
proposed an improved tolerance factor τ to more accurately predict the stability of perovskite structures,
[135]
addressing the limitations of the traditional Goldschmidt tolerance factor . Based on simple geometric
relationships, the Goldschmidt factor is somewhat effective for predicting the stability of oxide perovskites,
but performs inadequately in screening complex materials, especially halide-containing perovskites. To
overcome these limitations, Bartel and colleagues applied the sure independence screening and sparsifying
operator (SISSO) method to develop a new one-dimensional descriptor τ, incorporating information about
ionic radii and oxidation states. This significantly enhances prediction accuracy, achieving a 92% success
rate for the oxide, fluoride, and chloride perovskites dataset, demonstrating superior performance in
structural screening compared to the Goldschmidt factor. Figure 7A assesses the performance of τ in
predicting perovskite stability, illustrating the model accuracy across different chemical combinations and
its applicability to halide perovskites. Notably, the improved τ factor classifies perovskite stability and
provides a continuous stability probability estimate P(τ). By applying Platt scaling, Bartel et al. transformed
the model output into continuous probability values, allowing greater adaptability across various perovskite
[135]
types . Figure 7B shows a stability map for predicted double perovskites, with the lower triangle depicting
stability probabilities for Cs BB′Cl compounds and the upper triangle showing La BB′O compounds. These
6
2
2
6
probability maps, generated from Platt-scaled P(τ) values, use color gradients to indicate the likelihood of
forming stable perovskite structures across different ion combinations, highlighting potential stability. This
probability estimate provides researchers with an efficient screening tool for exploring potential perovskite
materials across broader chemical spaces. Additionally, extensive experimental validation enabled the model
to predict the stability of 23,314 potential double perovskites, providing a prioritized material list for further
investigation. This predictive approach lays a solid theoretical foundation for the functional design and
application of perovskite materials, with significant implications for their development in fields such as PVs
and electrocatalysis.

Building on the advancements in stability prediction by Bartel et al., Gu et al. introduced a novel approach
focused on the synthesizability of perovskite materials based on a graph convolutional neural network
(GCNN) combined with positive-unlabeled (PU) learning, addressing the limitations of traditional methods
[136]
in predicting the practical synthesizability of materials . Traditional stability prediction models, such as
convex hull energy calculations, primarily assess the thermodynamic stability of materials but are less
effective at predicting synthesizability under experimental conditions. To overcome this limitation, Gu and
colleagues pre-trained the GCNN model on actual synthesis data from the MP database and then applied it
to a perovskite-specific dataset . Using transfer learning, the model achieved significantly improved
[136]
synthesizability prediction accuracy across different perovskite structures, achieving a true positive rate of
95.7%. Figure 7C illustrates the model prediction accuracy and data distribution, highlighting the enhanced
performance of the GCNN and PU learning combination in identifying experimentally synthesized
materials as positive samples. Furthermore, the model demonstrated its effectiveness in predicting virtual
perovskites, identifying 179 out of 11,964 virtual perovskite candidates as having potential synthesizability
based on the model-generated crystal-likeness (CL). Figure 7D compares the GCNN model with screening
results based on the Goldschmidt rule. The lower left triangle in this figure presents synthesizability scores
for ABO perovskite compounds, with a green gradient indicating synthesizability likelihood (deeper green
3