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February 9, 2026Advanced Materials16 citationsOpen Access

Self‐Assembled Monolayers in p–i–n Perovskite Solar Cells: Molecular Design, Interfacial Engineering, and Machine Learning–Accelerated Material Discovery

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AUAsmat UllahYLYing LuoSWStefaan De Wolf

Key Points

  • This review aims to explore the role of self-assembled monolayers in optimizing hole transport layers for perovskite solar cells.
  • Comprehensive review of SAM-based HTLs evolution and performance
  • Analysis of structure-property-performance relationships governing SAM function
  • Evaluation of deposition techniques and operational stability under real-world conditions
  • Discussion on machine learning's role in material discovery and optimization
  • SAMs significantly enhance energy level alignment and interfacial defect passivation
  • Identified critical features affecting perovskite crystallization control
  • Outlined challenges in scalability and operational stability for commercial applications
  • Emphasized machine learning's potential in advancing SAM material optimization

Abstract

ABSTRACT Self‐assembled monolayers (SAMs) have precipitated a paradigm shift in the design of hole transport layers (HTLs) for p–i–n perovskite solar cells, emerging as the cornerstone of modern, high‐efficiency devices. This review comprehensively charts the evolution of SAM‐based HTLs from fundamental molecular‐level insights to their pivotal role in commercial‐scale applications and record‐breaking perovskite/silicon tandem cells. We delve into the intricate structure–property–performance relationships that govern SAMs’ function, examining how meticulous engineering of anchoring groups, π‐bridges, and functional headgroups dictates critical features such as energy level alignment, interfacial defect passivation, and perovskite crystallization control. The discussion extends beyond champion efficiencies to critically assess the scalability of deposition techniques, the limitations of operational stability under real‐world conditions, and the pathways for integration into tandem architectures. Furthermore, we highlight the transformative potential of machine learning in accelerating the discovery and optimization of next‐generation SAM materials. Finally, we provide a forward‐looking perspective on molecular design strategies required to overcome existing challenges and fully unlock SAM potential for stable, high‐performance photovoltaics.

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Cite This Study

Ullah et al. (2026) studied this question.

synapsesocial.com/papers/698979e9f0ec2af6756e7fb2https://doi.org/10.1002/adma.202520220
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Molecular Design and Interfacial Functions of Self‐Assembled Monolayers for Perovskite and Tandem Solar Cells2026 · 3 citations
  2. 2Self-Assembled Monolayer: Revolutionizing p-i-n Perovskite Solar Cells2025 · 42 citations
  3. 3Self‐Assembled Monolayers for High‐Performance Perovskite Solar Cells2025 · 27 citations
  4. 4Designing Efficient Inverted Perovskite Solar Cells with Self-Assembled Monolayer Hole Transport Layers2026
  5. 5Data‐Driven Design of Self‐Assembled Monolayers for High‐Efficiency Perovskite Solar Cells2026