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Review | Open Access

Energy Materials

Wang et al. Energy Mater. 2026, 6, 600064 DOI:10.20517/energymater.2026.36

The effect of additive engineering and machine
learning on high performance perovskite solar cells

Han Wang, Jiazhi Meng, Feiyu Kang , Guodan Wei *
*
Keywords:
Perovskite solar cells,
additive engineering,
machine learning, defect
passivation, data-driven
discovery
Citation: Wang, H.;
Meng, J.; Kang, F.; Wei, G.
The effect of additive
engineering and machine
learning on high
performance perovskite
solar cells. Energy Mater.
2026, 6, 600064.
https://dx.doi.org/10.20517
/energymater.2026.36
Received: 18 Mar 2026 Abstract
First Decision: 16 Apr
2026 Additive engineering has emerged as a powerful strategy for enhancing the efficiency and
Revised: 29 Apr 2026 stability of perovskite solar cells (PSCs), enabling precise control over crystallization
Accepted: 25 May 2026 kinetics, defect passivation, interfacial energetics, and long-term environmental stability.
Published: 17 Jun 2026
By controlling nucleation and crystal growth, and thereby optimizing film morphology,
Academic Editor: additives effectively suppress non-radiative recombination and ion migration, addressing
Soo Young Kim key challenges in the path toward commercialization. However, the conventional discovery
Copy Editor: process remains largely empirical and time-consuming. The integration of machine learning
Fangling Lan (ML) offers a promising avenue for data-driven screening, rational molecular design, and
Production Editor:
Fangling Lan accelerated optimization of additive systems. ML models trained on experimental datasets
and augmented with density functional theory and molecular dynamics simulations can
predict interactions between additives and perovskites, identify performance-determining
descriptors, and guide the discovery of novel functional molecules. This review
systematically outlines the multifaceted roles of additives in PSCs, from crystallization
regulation to interfacial stabilization. We further highlight the synergy between ML and
additive engineering, emphasizing its potential to establish a predictive, intelligent
framework for next-generation photovoltaic materials.

Institute of Materials Science, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen 518000, Guangdong,
China.

*Correspondence to: Prof. Guodan Wei, Prof. Feiyu Kang, Institute of Materials Science, Tsinghua Shenzhen International Graduate
School, Tsinghua University, Shenzhen 518000, Guangdong, China. E-mail: weiguodan@sz.tsinghua.edu.cn; fykang@mail.tsinghua.edu.cn

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