Topological Data Analysis Combined with High-Throughput Computational Screening of Hydrophobic Metal-Organic Frameworks: Application to the Adsorptive Separation of C3 Components.

Yujuan Yang, Shuya Guo, Shuhua Li, Yufang Wu, Zhiwei Qiao

Abstract read

In one paragraph

Article in Nanomaterials (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed

–field-weighted citation impact

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3 · Its place in the literature

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1 citing paper in PubMed.

A Review of Topological Data Analysis and Topological Deep Learning in Molecular Sciences.Journal of chemical information and modeling · 2025
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5 · Who and what money

Authors and funding

5 authors.

Yujuan YangGuangzhou Key Laboratory for New Energy and Green Catalysis, School of Chemistry and Chemical Engineering, Guangzhou University, Guangzhou 510006, China.

Shuya GuoGuangzhou Key Laboratory for New Energy and Green Catalysis, School of Chemistry and Chemical Engineering, Guangzhou University, Guangzhou 510006, China.

Shuhua LiGuangzhou Key Laboratory for New Energy and Green Catalysis, School of Chemistry and Chemical Engineering, Guangzhou University, Guangzhou 510006, China.

Yufang WuGuangzhou Key Laboratory for New Energy and Green Catalysis, School of Chemistry and Chemical Engineering, Guangzhou University, Guangzhou 510006, China.ORCID 0000-0001-6354-883X

Zhiwei QiaoGuangzhou Key Laboratory for New Energy and Green Catalysis, School of Chemistry and Chemical Engineering, Guangzhou University, Guangzhou 510006, China.ORCID 0000-0002-1264-3762

Funding

Guangzhou University Postgraduate Innovation Ability Training Subsidy Plan 2022GDJC-M08the Guangzhou Municipal Science and Technology Project 202102020875the Guangzhou Municipal Science and Technology Project 202201020169the National Natural Science Foundation of China 21676094the National Natural Science Foundation of China 21978058the Natural Science Foundation of Guangdong Province 2020A1515010800the Natural Science Foundation of Guangdong Province 2022A1515011446the Pearl River Talent Recruitment Program 2019QN01L255the R & D Program of the Joint Institute of GZHU and ICoST GI202102

6 · The paper itself

Abstract

The shape and topology of pores have significant impacts on the gas storage properties of nanoporous materials. Metal-organic frameworks (MOFs) are ideal materials with which to tailor to the needs of specific applications, due to properties such as their tunable structure and high specific surface area. It is, therefore, particularly important to develop descriptors that accurately identify the topological features of MOF pores. In this work, a topological data analysis method was used to develop a topological descriptor, based on the pore topology, which was combined with the Extreme Gradient Boosting (XGBoost) algorithm to predict the adsorption performance of MOFs for methane/ethane/propane. The final results show that this descriptor can accurately predict the performance of MOFs, and the introduction of the topological descriptor also significantly improves the accuracy of the model, resulting in an increase of up to 17.55% in the

Indexed as

adsorptionmetal–organic frameworks (MOFs)petroleumtopological data analysis (TDA)topology of pores

Identifiers

PMID38334569

DOI10.3390/nano14030298

PMCPMC10857702

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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.