Evidence map›Paper›PMID 42529256›Full record

ArticleJACS Au2026

Data-Driven Synthesis of Covalent Organic Frameworks via Machine Learning with Integrated Success-Failure Data.

Bing Ma, Qianqian Yan, Wei Zhou, Junhao Wu, Lipiao Bao, Xinhui Lu, Jixia Qiu, Yuanyuan Zhu, Xiao Wang, Dong Zhai and 4 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in JACS Au, 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.

Bing MaSchool of Chemistry and Chemical Engineering, Hainan University, 58 Renmin Avenue, Haikou 570228, Hainan.
Qianqian YanSchool of Chemistry and Chemical Engineering, Hainan University, 58 Renmin Avenue, Haikou 570228, Hainan.
Wei ZhouSchool of Chemistry and Chemical Engineering, Hainan University, 58 Renmin Avenue, Haikou 570228, Hainan.ORCID https://orcid.org/0000-0003-0006-0671
Junhao WuSchool of Chemistry and Chemical Engineering, Hainan University, 58 Renmin Avenue, Haikou 570228, Hainan.
Lipiao BaoSchool of Chemistry and Chemical Engineering, Hainan University, 58 Renmin Avenue, Haikou 570228, Hainan.ORCID https://orcid.org/0000-0002-6308-1181
Xinhui LuSchool of Chemistry and Chemical Engineering, Hainan University, 58 Renmin Avenue, Haikou 570228, Hainan.
Jixia QiuSchool of Chemistry and Chemical Engineering, Hainan University, 58 Renmin Avenue, Haikou 570228, Hainan.
Yuanyuan ZhuSchool of Chemistry and Chemical Engineering, Hainan University, 58 Renmin Avenue, Haikou 570228, Hainan.
Xiao WangSchool of Chemistry and Chemical Engineering, Hainan University, 58 Renmin Avenue, Haikou 570228, Hainan.
Dong ZhaiInstitute of Frontier Chemistry, School of Chemistry and Chemical Engineering, Shandong University, Qingdao 266237, P. R. China.ORCID https://orcid.org/0000-0003-3155-4607
Chengcheng LiuInstitute of Frontier Chemistry, School of Chemistry and Chemical Engineering, Shandong University, Qingdao 266237, P. R. China.ORCID https://orcid.org/0000-0003-2504-4627
Sheng ZhangSchool of Chemistry and Chemical Engineering, Hainan University, 58 Renmin Avenue, Haikou 570228, Hainan.ORCID https://orcid.org/0000-0001-9370-0548
Weiqiao DengInstitute of Frontier Chemistry, School of Chemistry and Chemical Engineering, Shandong University, Qingdao 266237, P. R. China.ORCID https://orcid.org/0000-0002-3671-5951
Xing LuSchool of Chemistry and Chemical Engineering, Hainan University, 58 Renmin Avenue, Haikou 570228, Hainan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Covalent organic frameworks (COFs) have emerged as a versatile class of porous materials with promising applications in catalysis, energy storage, and gas adsorption. However, their synthesis remains a major bottleneck primarily due to the widespread reliance on inefficient trial-and-error approaches that waste resources and delay discovery. Herein, we address this challenge by integrating success and failure data: we curated 1822 in-house failed synthesis records and extracted 2603 successful cases from the literature. A random forest machine learning (ML) model, selected for its robustness with complex experimental data sets, achieved 91% accuracy in solvent prediction and demonstrated strong predictive performance for reaction temperature (

Indexed as

covalent organic frameworksdata-driven synthesismachine learningrandom forest modelSHAP analysissuccess and failure data

Identifiers

PMID42529256
PMCPMC13417185

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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.