Rapidly tailor metal–organic frameworks for arsenate removal using graph convolutional neural networks-Reference-Cited by-同舟云学术

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Rapidly tailor metal–organic frameworks for arsenate removal using graph convolutional neural networks

Published:2025-02 Issue: Volume:354 Page:129334
ISSN:1383-5866
Container-title:Separation and Purification Technology
language:en
Short-container-title:Separation and Purification Technology

Author:

Lin Zuhong,Chen Jiarong,Fang Ying,Deng Shi-hai,Li Haipu^ORCID,Yang Ying,Yao Jingjing^ORCID

Funder

National Key Research and Development Program of China

Fundamental Research Funds for Central Universities of the Central South University

Publisher

Elsevier BV

Reference62 articles.

1. Structure-Mechanical Stability Relations of Metal-Organic Frameworks via Machine Learning;Moghadam;Matter.,2019

2. Structural Regulation and Light Hydrocarbon Adsorption/Separation of Three Zirconium-Organic Frameworks Based on Different V-Shaped Ligands;Gu;ACS Appl. Mater. Interfaces,2021

3. Theoretical studies of metal-organic frameworks: Calculation methods and applications in catalysis, gas separation, and energy storage;Hai;Coord. Chem. Rev.,2022

4. Recent advances in computational modeling of MOFs: From molecular simulations to machine learning;Demir;Coord. Chem. Rev.,2023

5. Metal–organic frameworks (MOFs) for the removal of emerging contaminants from aquatic environments;Dhaka;Coord. Chem. Rev.,2019

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