Machine Learning for Gas Capture in Ionic Liquids: Current Status and Future Trends.

Guocai Tian, Zhiqiang Hu, Ranran Geng

Abstract readReview

In one paragraph

Review in Molecules (Basel, Switzerland), 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

3 authors.

Guocai TianState Key Laboratory of Complex Non-Ferrous Metal Resource Clean Utilization, Faculty of Metallurgical and Energy Engineering, Kunming University of Science and Technology, Kunming 650093, China.ORCID 0000-0002-0913-6903

Zhiqiang HuState Key Laboratory of Complex Non-Ferrous Metal Resource Clean Utilization, Faculty of Metallurgical and Energy Engineering, Kunming University of Science and Technology, Kunming 650093, China.

Ranran GengState Key Laboratory of Complex Non-Ferrous Metal Resource Clean Utilization, Faculty of Metallurgical and Energy Engineering, Kunming University of Science and Technology, Kunming 650093, China.

Funding

National Natural Science Foundation of China 52364042Yunnan Fundamental Research Projects 202501AS070127

6 · The paper itself

Abstract

Ionic liquids, as green gas solubility media, have great potential for applications in carbon capture, industrial waste gas purification, and other fields. However, the massive combination of anions and cations makes their screening extremely difficult. Machine learning can break through the bottleneck of traditional experiments and simulations and achieve high-throughput prediction of gas solubility in ionic liquids. This article provides a systematic review of the research progress of machine learning in predicting the gas solubility performance of ionic liquids. The classification and modeling process of machine learning, the construction and performance of machine learning prediction models for the solubility of gases such as CO

Indexed as

gas capturegreen solventsionic liquidsmachine learningQSARsolubility

Identifiers

PMID42451662

DOI10.3390/molecules31132293

PMCPMC13363378

What OpenQuestion holds

Textmetadata

LicenceCC BY

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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.