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Page 2 of 34 Wang et al. Energy Mater. 2026, 6, 600064

INTRODUCTION
Over the past decade, metal halide perovskite solar cells (PSCs) have emerged as a leading candidate for
next-generation photovoltaic technologies, driven by their exceptional optoelectronic properties [1,2] ,
including strong light absorption [3,4] , long carrier diffusion lengths [5,6] , tunable direct bandgaps [7,8] , and
intrinsic defect tolerance [9,10] , as well as compatibility with low-cost [11,12] , solution-based fabrication [13,14] . Since
their initial demonstration in 2009 with a power conversion efficiency (PCE) of just 3.8% , single-junction
[15]
PSCs have rapidly advanced, achieving certified efficiencies of over 27.0% , which now rival those of
[16]
commercial crystalline silicon solar cells. Despite these advances, operational instability arising from
environmental stressors such as heat [17,18] , moisture [19,20] , light [21,22] , and electrical bias , as well as ion
[23]
migration [4,24] and defect accumulation in polycrystalline films, continues to limit the long-term stability
[25]
and durability of PSCs. Furthermore, scaling from laboratory-scale devices to large-area modules introduces
significant hurdles in achieving uniform, reproducible, and stable perovskite films, posing considerable
challenges to both device performance and widespread commercialization [26,27] .

To overcome these limitations, additive engineering has become one of the most versatile and effective
strategies. By incorporating trace amounts of functional molecules into perovskite precursor solutions or
interfacial layers, researchers have achieved substantial improvements in film morphology [28,29] , defect
passivation [10,30] , energy-level alignment [31,32] , and long-term stability [33,34] . Additives serve multiple critical
functions, including modulation of crystallization kinetics to promote the formation of large-grain,
pinhole-free films [35,36] , passivation of undercoordinated ions and vacancies to suppress non-radiative
recombination and ion migration [37,38] , and optimization of interfacial energetics to enhance charge extraction
while providing protective barriers against environmental degradation [39,40] . Collectively, these
multifunctional effects establish additive engineering as a pivotal approach in advancing PSC performance
and stability. However, conventional trial-and-error strategies are inherently inefficient in navigating the
immense chemical space and deciphering the intricate interplay between additives, perovskite chemistry, and
device architecture. The vast chemical diversity of potential additives and their complex, often
interdependent interactions with perovskite systems pose significant challenges for rational design, thereby
necessitating the integration of advanced computational and data-driven methodologies. In this regard,
machine learning (ML) is emerging as a transformative tool for accelerating additive discovery and
optimization. ML models trained on experimental datasets and high-throughput simulations, using
molecular descriptors as input features, can rapidly identify promising candidates, predict their impact on
crystallization dynamics, defect passivation, and interfacial stability, and propose novel molecular scaffolds
that are difficult to identify through conventional approaches . These synergistic advances highlight the
[41]
potential of merging additive engineering with ML to establish predictive design frameworks, ultimately
enabling the development of high-performance, stable, and scalable perovskite photovoltaics.

Although extensive research has demonstrated the functional versatility of diverse additives in perovskite
systems, the absence of unified design principles and the trial-and-error nature of additive discovery
continue to hinder rational device optimization. At the same time, the advent of ML and multiscale
simulations provides powerful tools to accelerate the exploration of the vast chemical space of potential
additives, offering significant opportunities to establish predictive frameworks for additive design. Given
these opportunities and challenges, a systematic understanding of additive engineering in PSCs is both timely
and necessary. In this review, we provide a comprehensive overview and categorization of functional
additives, analyze their working mechanisms within the bulk and across interfaces, and highlight the
integration of ML with multiscale simulations to enable predictive materials design in perovskite
photovoltaics. By bridging mechanistic insights with data-driven approaches, this work aims to outline the
state-of-the-art strategies, identify key challenges, and propose future directions for advancing PSCs toward
high efficiency, stability, and scalability.