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Page 26 of 34 Wang et al. Energy Mater. 2026, 6, 600064

Comprehensive
multi-level features
Experimental data encompassing structural
meticulously extracted from physical and electronic
peer reviewed publications Extreme properties prominently Benzodithiophene PCE = 25.36%,
published between 2015 and gradient featuring molecular PCE (Voc) terthiophene rhodanine Voc = 1.186 V [169]
2024 focusing on PSCs and boosting (XGB) fingerprints steric derivative (BTR-Cl)
their passivation materials hindrance charge transfer
distance and frontier
molecular orbital energies
PCE: Power conversion efficiency; HTL: hole transport layer; MSC: Molecular Scaffold Classifier; JTVAE: Junction Tree Variational Autoencoder;
Co-PAS: co-pilot for perovskite additive screener.

rational design of environmentally stable systems that maintain structural integrity under operational
conditions. A crucial next step is the integration of life-cycle assessment (LCA) metrics into computational
workflows, allowing simultaneous optimization of device efficiency and environmental impact. Although
currently underexplored, this approach holds significant promise for aligning photovoltaic development with
the principles of green chemistry and the circular economy.

A key frontier lies in the development of multifunctional additives capable of simultaneously mitigating
multiple degradation pathways, including targeted defect passivation, suppression of ion migration, and
stabilization of chemically active phases. ML-driven frameworks are already demonstrating success in
identifying synergistic effects within composite systems, revealing complex chemical interactions such as
deprotonation and proton exchange mechanisms that are difficult to access through conventional
trial-and-error approaches. Looking ahead, significant opportunities exist to expand additive discovery
beyond small organic molecules to include emerging material classes such as two-dimensional nanosheets,
quantum dots, covalent organic frameworks (COFs), and supramolecular assemblies. While many of these
architectures remain at the proof-of-concept stage, algorithmic prediction can accelerate their maturation by
linking molecular structures to multi-output device responses, paving the way for scalable solutions.

The most transformative advances are likely to emerge from closed-loop discovery paradigms that unify ML,
high-throughput experimentation, and multiscale simulations. Autonomous robotic platforms have already
demonstrated the feasibility of such integrated workflows, yet their specific application to additive
optimization in PSCs remains early-stage [172] . By integrating ML with DFT and molecular dynamics (MD)
simulations, researchers can link such atomistic descriptors as charge density distributions and vibrational
spectra to macroscopic device performance metrics, thereby establishing design principles applicable across
diverse fabrication protocols [173] . Future progress will hinge on scaling these integrated workflows to
industrially relevant dimensions, ultimately transforming additive engineering into a self-driving materials
discovery process that is both efficient and predictive. Collectively, these converging pathways will drive the
technology from empirical observation to knowledge-driven innovation, accelerating the transition from
laboratory breakthroughs to commercial deployment.

CONCLUSION
Additive engineering has proven indispensable in advancing PSC technology, offering a versatile toolkit to
simultaneously optimize film quality, passivate defects, stabilize interfaces, and align energy levels. Through
rational molecular design, additives have evolved into multifunctional agents that actively govern the
perovskite formation process from the precursor solution to the final crystalline film, thereby enabling
precise control over material properties and significantly enhancing device performance. Yet, the historical
reliance on trial-and-error experimentation severely limits the speed and scalability of additive discovery.
This challenge is being systematically addressed through the strategic integration of ML, which is
transforming additive engineering into a predictive and rational science. As summarized in this review, this