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al. discovered that SACs in the pyrrole-type coordination could exhibit superior catalytic activity towards
NRR when the d-orbitals are exactly half occupied and the difference in covalent radius is approximately
[48]
140 pm. Their model was based on GBR and employed SHAP methods to interpret the results . Recently,
Zhong et al. used active ML to accelerate DFT screening for CO reduction electrocatalysts to ethylene,
2
where t-SNE visualization reveals that Cu-Al alloys have the highest density of adsorption sites with optimal
CO binding energies. This finding successfully guided experiment to achieve record-high Faradaic
efficiency over 80% at 400 mA/cm 2[109] .

DISCUSSION AND PERSPECTIVES
PIML has demonstrated great potential in developing high-performance electrocatalysts at reduced cost and
time. However, there are still some limitations. First, the integration of physical or chemical insights into
PIML models often adds complexity to the model training process, especially when it comes to large
datasets. In essence, more complex physical or chemical constraints can result in higher interpretability but
lower efficiency, and vice versa [110,111] . As a result, the trade-off between interpretability and efficiency needs
to be navigated. Second, the use of prior knowledge from experts, such as physics- or chemistry-based
descriptors, remains essential for PIML models. While some PIML models, such as equivariant GNNs, are
highly integrated with physical constraints, their complexity can make it difficult to interpret the underlying
physics or chemistry. The combination of XAI with these models may offer a solution to improve their
interpretability. Last but not least, a lack of benchmarks to evaluate the interpretability performance of
PIML models remains a challenge.

Despite these limitations, PIML is poised to become an indispensable tool in materials science. Notable
progress has been made, such as the work by Szymanski et al., who established an automated laboratory (A-
lab) and identified 41 novel materials from 58 candidates in just 17 days . This achievement encourages
[112]
further development of more effective A-labs, where PIML will play a pivotal role. In the future, PIML-
based generative models may directly generate physically and chemically meaningful candidates, providing
experts with valuable physical/chemical insight and reducing the observation error. Moreover, integrating
LLMs with PIMLs could enhance the interactivity of models with humans, enabling more efficient
communication and decision-making. In addition, integrating robotics with these models will accelerate the
synthesis and characterization of new materials with minimal human intervention. Figure 4 shows the
potential role of PIMLs in an A-lab. In this process, researchers can propose their requirements to LLMs,
which will generate model parameters to PIML for experimental predictions. These predictions will guide
robots to synthesize new materials, whose structures will be analyzed and returned to researchers by the
PIML-based generative models. Data from these new materials will be added to the database for training
next-generation PIML. This active learning strategy will be added to the database to train future iterations of
PIML models, creating an active learning loop. This strategy holds the promise of transforming the
materials science field, with PIML playing an increasingly critical role in driving scientific discovery.

CONCLUSION
PIML has emerged as a transformative approach in electrocatalyst development, combining predictive
power with a deeper understanding of the underlying mechanisms. Despite significant progress, several
challenges remain in data quality, model interpretability, and computational efficiency. As these challenges
are addressed, PIML approaches are poised to play an increasingly important role in the development of
efficient, cost-effective electrocatalysts for clean energy applications. By leveraging the synergy between ML
and physical sciences, PIML has the potential to accelerate the discovery of novel materials and improve the
performance of existing electrocatalysts. This progress may significantly contribute to the global transition
to sustainable energy systems, helping address pressing environmental concerns and enabling a cleaner,
more efficient energy future.