Evidence map›Paper›PMID 39337733›Full record

ArticleMicromachines2024

Machine Learning-Guided Cycle Life Prediction for Electrochromic Devices Based on Deuterium and Water Mixing Solvent.

Yitong Wu, Sifan Kong, Qingxin Yao, Muyun Li, Huayi Lai, Duoyu Sun, Qingyue Cai, Zelin Qiu, Honglong Ning, Yong Zhang

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Micromachines, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. An Overview of Chromic Transition Metal Oxide Thin Films.Materials (Basel, Switzerland) · 2026
    Review
  3. Review
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

10 authors.

Yitong WuSchool of Electronics and Information Engineering, South China Normal University, Foshan 528225, China.ORCID 0009-0004-9980-2130
Sifan KongSchool of Software, South China Normal University, Foshan 528225, China.
Qingxin YaoSchool of Electronics and Information Engineering, South China Normal University, Foshan 528225, China.
Muyun LiState Key Laboratory of Luminescent Materials and Devices, School of Materials Science and Engineering, South China University of Technology, Guangzhou 510640, China.
Huayi LaiAberdeen Institute of Data Science and Artificial Intelligence, South China Normal University, Foshan 528225, China.
Duoyu SunSchool of Software, South China Normal University, Foshan 528225, China.ORCID 0009-0009-0440-0953
Qingyue CaiState Key Laboratory of Luminescent Materials and Devices, School of Materials Science and Engineering, South China University of Technology, Guangzhou 510640, China.
Zelin QiuSchool of Semiconductor Science and Technology, South China Normal University, Foshan 528225, China.
Honglong NingState Key Laboratory of Luminescent Materials and Devices, School of Materials Science and Engineering, South China University of Technology, Guangzhou 510640, China.ORCID 0000-0001-9518-5738
Yong ZhangSchool of Semiconductor Science and Technology, South China Normal University, Foshan 528225, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Electrochromic devices have demonstrated considerable potential in a range of applications, including smart windows and automotive rearview mirrors. However, traditional cycle life testing methods are time-consuming and require significant resources to process a substantial amount of generated data, which presents a significant challenge and remains an urgent issue to be addressed. To address this challenge, we proposed the use of Long Short-Term Memory (LSTM) networks to construct a prediction model of the cycle life of electrochromic devices and introduced an interpretable analysis method to further analyze the model's predictive capabilities. The original dataset used for modeling was derived from preliminary experiments conducted under 1000 cycles of six devices prepared with varying mixing ratios of heavy water (D

Indexed as

cycle lifeelectrochromismlong short-term memorymachine learning

Identifiers

PMID39337733
PMCPMC11433885

What OpenQuestion holds

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Registered trials

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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.