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Wang et al. Energy Mater. 2026, 6, 600064 Page 27 of 34

transformation follows a clear evolutionary trajectory, advancing from data-driven analysis and
high-throughput screening to physics-informed prediction. Despite these algorithmic advancements, several
critical limitations remain. Current predictive frameworks predominantly focus on PCE as a singular target
property, neglecting essential requirements such as long-term stability and industrial scalability.
Furthermore, the survivorship bias inherent in historical literature datasets and the lack of standardized
testing protocols continue to limit the generalization of these models. Looking ahead, the convergence of ML
and additive engineering will drive the next wave of innovation in perovskite photovoltaics. Future efforts
must explore broader molecular spaces using deep generative models and rigorous physical descriptors,
shifting the paradigm from empirical correlation to physics-informed design. Multifunctional additive design
should expand to encompass complex material architectures while incorporating sustainability through LCA
and green chemistry principles. Ultimately, the most transformative advances will emerge from closed-loop
discovery paradigms that unify multiscale simulations, high-throughput experimentation, and autonomous
robotic platforms. By scaling these integrated workflows to industrially relevant dimensions, the field will
move toward a self-driven materials discovery process, accelerating the transition of efficient, stable, and
environmentally friendly PSCs from laboratory to commercial deployment.

DECLARATIONS

Authors’ contributions
Performed the literature search, conducted the synthesis, and wrote the manuscript: Wang, H.
Assisted with study design: Meng, J.
Revised the manuscript for intellectual clarity: Kang, F.
Reviewed the final manuscript: Wei, G.
All authors contributed to the conceptualization of this review.

Availability of data and materials
Not applicable.

AI and AI-assisted tools statement
Not applicable.

Financial support and sponsorship
This work is supported by funding from National Natural Science Foundation of China (52027817,
52572175), Tsinghua Shenzhen International Graduate School Overseas Research Cooperation Fund
(HW2024008).

Conflicts of interest
All authors declared that there are no conflicts of interest.

Ethical approval and consent to participate
Not applicable.

Consent for publication
Not applicable.

Copyright
© The Author(s) 2026.

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