Machine Learning for Gas Adsorption in Metal–Organic Frameworks: A Review on Predictive Descriptors

IF 3.8 3区 工程技术 Q2 ENGINEERING, CHEMICAL
I-Ting Sung, Ya-Hung Cheng, Chieh-Ming Hsieh, Li-Chiang Lin
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Abstract

This review addresses a critical gap in the literature by focusing on the features (or descriptors) used in machine learning (ML) studies to predict gaseous adsorption properties in metal–organic frameworks (MOFs). Although ML approaches for predicting adsorption properties in MOFs have been extensively reported in recent years, features employed in ML models have not been thoroughly reviewed. A comprehensive review of these features is crucial since they form the foundation for building effective predictive models. These models are also key to facilitating the inverse design of MOFs, as they can be used to efficiently predict the performance of material candidates and explore the structure–property relationship, guiding the creation of optimal MOF structures. Furthermore, ML models can also be naturally employed in inverse design approaches, such as encoder–decoder architectures. This review starts with a brief overview of the importance and applications of MOFs in various fields, followed by a discussion of the historical milestones of MOFs in computational research, highlighting the critical role of ML. This review then discusses traditional features and introduces newly proposed distinctive features, referred to as “beyond traditional features”, that have been reported to date. Furthermore, generalized ML models for predicting the adsorption properties of different gases are also outlined. Finally, we offer future outlooks on ML-assisted computational searches for MOFs in adsorption applications. Overall, this review aims to help researchers grasp current developments and offer insights into future directions in this area.

Abstract Image

金属-有机框架中气体吸附的机器学习:预测描述符综述
这篇综述通过关注机器学习(ML)研究中用于预测金属有机框架(mof)中气体吸附特性的特征(或描述符),解决了文献中的一个关键空白。尽管近年来预测mof中吸附特性的ML方法已经被广泛报道,但ML模型中使用的特征尚未得到彻底的审查。对这些特征的全面回顾是至关重要的,因为它们构成了构建有效预测模型的基础。这些模型也是促进MOF反设计的关键,因为它们可以有效地预测候选材料的性能并探索结构-性能关系,指导最佳MOF结构的创建。此外,机器学习模型也可以自然地用于逆向设计方法,如编码器-解码器架构。本文首先简要概述了mof在各个领域的重要性和应用,然后讨论了mof在计算研究中的历史里程碑,突出了ML的关键作用。然后讨论了传统特征,并介绍了迄今为止报道的新提出的独特特征,称为“超越传统特征”。此外,还概述了用于预测不同气体吸附性质的广义ML模型。最后,我们展望了机器学习辅助计算搜索mof在吸附中的应用前景。总的来说,这篇综述旨在帮助研究人员掌握当前的发展,并为该领域的未来发展方向提供见解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Industrial & Engineering Chemistry Research
Industrial & Engineering Chemistry Research 工程技术-工程:化工
CiteScore
7.40
自引率
7.10%
发文量
1467
审稿时长
2.8 months
期刊介绍: ndustrial & Engineering Chemistry, with variations in title and format, has been published since 1909 by the American Chemical Society. Industrial & Engineering Chemistry Research is a weekly publication that reports industrial and academic research in the broad fields of applied chemistry and chemical engineering with special focus on fundamentals, processes, and products.
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