Evidence map›Paper›PMID 42479286›Full record

ArticleNano-micro letters2026

Artificial Intelligence-Guided Cosolvent Design for High-Performance Perovskite/Silicon Tandem Solar Cells.

Lu Liu, Xinying Cai, Bita Farhadi, Xinrui Dong, Kai Wang, Yufei Shao, Shulin Wang, Jiaxue You, Wanyi Li, Hao-Chung Kuo and 4 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Nano-micro letters, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

14 authors.

Lu Liu *State Key Laboratory of Photoelectric Conversion and Utilization of Solar Energy, Center of Materials Science and Optoelectronics Engineering, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, 116023, People's Republic of China.
Xinying Cai *Department of Materials Science and Engineering, City University of Hong Kong, Kowloon, 999077, Hong Kong, People's Republic of China.
Bita FarhadiEIT Data Science and Communication College, Zhejiang Yuexiu University, Shaoxing, 312030, People's Republic of China.
Xinrui DongState Key Laboratory of Photoelectric Conversion and Utilization of Solar Energy, Center of Materials Science and Optoelectronics Engineering, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, 116023, People's Republic of China.
Kai WangState Key Laboratory of Photoelectric Conversion and Utilization of Solar Energy, Center of Materials Science and Optoelectronics Engineering, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, 116023, People's Republic of China. wangkai@dicp.ac.cn.
Yufei ShaoState Key Laboratory of Photoelectric Conversion and Utilization of Solar Energy, Center of Materials Science and Optoelectronics Engineering, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, 116023, People's Republic of China.
Shulin WangState Key Laboratory of Photoelectric Conversion and Utilization of Solar Energy, Center of Materials Science and Optoelectronics Engineering, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, 116023, People's Republic of China.
Jiaxue YouDepartment of Materials Science and Engineering, City University of Hong Kong, Kowloon, 999077, Hong Kong, People's Republic of China.
Wanyi LiState Key Laboratory of Photoelectric Conversion and Utilization of Solar Energy, Center of Materials Science and Optoelectronics Engineering, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, 116023, People's Republic of China.
Hao-Chung KuoSemiconductor Research Center, Hon Hai Research Institute, Taipei, Taiwan, People's Republic of China.
Hanying WangSchool of New Energy, Yulin University, Yulin, 719000, People's Republic of China.
Dong YangState Key Laboratory of Photoelectric Conversion and Utilization of Solar Energy, Center of Materials Science and Optoelectronics Engineering, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, 116023, People's Republic of China. dongyang@dicp.ac.cn.
Alex K-Y JenDepartment of Materials Science and Engineering, City University of Hong Kong, Kowloon, 999077, Hong Kong, People's Republic of China. alexjen@cityu.edu.hk.
Shengzhong Frank LiuState Key Laboratory of Photoelectric Conversion and Utilization of Solar Energy, Center of Materials Science and Optoelectronics Engineering, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, 116023, People's Republic of China. szliu@dicp.ac.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Realizing high-performance perovskite/silicon tandem solar cells requires precise control of wide-bandgap perovskite crystallization. Solvent engineering is the most direct lever for this task; yet, its intricate, multi-variable mechanisms defy intuition-driven design. Herein, we overcome this bottleneck by pioneering a retrieval-augmented large language model to screen > 8000 solvents, identifying γ-valerolactone (GVL) as a non-toxic, high-performance cosolvent. It is found that the GVL strongly coordinates FA

Indexed as

Green solventHigh crystallinityMachine learningTandem solar cellsWide-bandgap perovskite

Identifiers

PMID42479286
PMCPMC13388578

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.