Evidence map›Paper›PMID 40801492›Full record

ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2025

Accelerated Development of Vapor Deposition Technology for Efficient Perovskite Solar Cells via Accurate and Practical Machine Learning Tools.

Long Luo, Ziyang Gao, Muyi Fang, Yu Shen, Shuaizhang Chen, Yuchen Bu, Junjie Gu, Cheng Hu, Jianning Ding

Abstract read
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In one paragraph

Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

9 authors.

Long LuoCollege of Physical Science and Technology, Institute of Technology for Carbon Neutralization, Yangzhou University, Jiangsu, 225009, China.ORCID https://orcid.org/0009-0001-1434-7625
Ziyang GaoCollege of Chemical and Environmental Engineering, Wuhan Polytechnic University, Hubei, 430000, China.
Muyi FangCollege of Physical Science and Technology, Institute of Technology for Carbon Neutralization, Yangzhou University, Jiangsu, 225009, China.
Yu ShenCollege of Physical Science and Technology, Institute of Technology for Carbon Neutralization, Yangzhou University, Jiangsu, 225009, China.
Shuaizhang ChenCollege of Physical Science and Technology, Institute of Technology for Carbon Neutralization, Yangzhou University, Jiangsu, 225009, China.
Yuchen BuCollege of Physical Science and Technology, Institute of Technology for Carbon Neutralization, Yangzhou University, Jiangsu, 225009, China.
Junjie GuCollege of Physical Science and Technology, Institute of Technology for Carbon Neutralization, Yangzhou University, Jiangsu, 225009, China.
Cheng HuBusiness School, Institute of Technology for Carbon Neutralization, Yangzhou University, Jiangsu, 225009, China.ORCID https://orcid.org/0000-0003-3962-8519
Jianning DingCollege of Physical Science and Technology, Institute of Technology for Carbon Neutralization, Yangzhou University, Jiangsu, 225009, China.

Funding

Natural Science Foundation of Jiangsu Province BK20240928Natural Science Foundation of Yangzhou City YZ2024178Outstanding Doctor Program of Yangzhou City 'Lv Yang Jin Feng' YZLYJFJH2023YXBS114
6 · The paper itself

Abstract

The development of vapor deposition technology will accelerate the process of perovskite solar cells (PSCs) moving from laboratory scale to industrialization. However, the multi-dimensional and complex parameter space will inevitably increase the cost of trial and error, especially for high-energy-consuming and long-cycle vapor deposition technologies. This study employed an integrated-feature dataset encompassing macro- and micro-features to enhance the accuracy, robustness, and interpretability of an ETree machine learning (ML) model for power conversion efficiency (PCE) prediction, achieving an impressive coefficient of determination value of 0.9464 and root mean square error value of 1.27%. Through SHAP analysis, Monte Carlo simulations, and parameter space exploration, an optimal FTO/SnO

Indexed as

Machine learningPerovskite solar cellsVapor deposition technology

Identifiers

PMID40801492
PMCPMC12622531

What OpenQuestion holds

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LicenceCC BY
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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.