Unified physio-thermodynamic descriptors via learned CO2 adsorption properties in metal-organic frameworks

IF 11.9 1区 材料科学 Q1 CHEMISTRY, PHYSICAL
Emily Lin, Yang Zhong, Gang Chen, Sili Deng
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引用次数: 0

Abstract

The large design space of metal-organic frameworks (MOFs) has prompted the utilization of deep learning to drive material design. Nonetheless, the prediction of key thermodynamic properties, such as heat of adsorption (\(\Delta {H}_{{\rm{ads}}}\)), remains largely unexplored for CO2 adsorption in MOFs. Herein, we present IsothermNet, a high-throughput graph neural network designed to estimate uptake and \(\Delta {H}_{{\rm{ads}}}\) over 0–50 bars, enabling high-quality full isotherm reconstruction (PCC: 0.73–0.95 [uptake], 0.76–0.88 [\(\Delta {H}_{{\rm{ads}}}\)]). We further bridged these adsorption properties to uptake behaviors (i.e., isotherm shapes/types) and structural information by performing detailed ablation studies to investigate the relative importance of local and global features in relation to predictive performance. This comparative analysis facilitated the discovery of a (1) physically-interpretable and (2) analytically-derived universal descriptor set capable of illustrating interdependencies between easily-computed, accessible textural information and extrinsic adsorption properties. When used cooperatively with IsothermNet, these descriptors enable efficient material screening, accelerating high-performance MOF discovery for CO2 capture.

Abstract Image

统一的物理热力学描述符通过学习二氧化碳吸附性质在金属有机框架
金属有机框架(MOFs)的巨大设计空间促使了深度学习技术在材料设计中的应用。尽管如此,预测关键的热力学性质,如吸附热(\(\Delta {H}_{{\rm{ads}}}\)),在很大程度上仍未探索二氧化碳在mof中的吸附。在此,我们提出了IsothermNet,这是一个高通量的图形神经网络,旨在估计0-50 bar的摄取和\(\Delta {H}_{{\rm{ads}}}\),实现高质量的全等温线重建(PCC: 0.73-0.95[摄取],0.76-0.88 [\(\Delta {H}_{{\rm{ads}}}\)])。我们进一步将这些吸附特性与吸收行为(即等温线形状/类型)和结构信息联系起来,通过进行详细的消融研究来研究局部和全局特征与预测性能的相对重要性。这种比较分析有助于发现(1)物理可解释和(2)解析衍生的通用描述符集,能够说明易于计算、可获取的纹理信息和外在吸附特性之间的相互依赖性。当与IsothermNet协同使用时,这些描述符可以实现高效的材料筛选,加速发现二氧化碳捕获的高性能MOF。
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来源期刊
npj Computational Materials
npj Computational Materials Mathematics-Modeling and Simulation
CiteScore
15.30
自引率
5.20%
发文量
229
审稿时长
6 weeks
期刊介绍: npj Computational Materials is a high-quality open access journal from Nature Research that publishes research papers applying computational approaches for the design of new materials and enhancing our understanding of existing ones. The journal also welcomes papers on new computational techniques and the refinement of current approaches that support these aims, as well as experimental papers that complement computational findings. Some key features of npj Computational Materials include a 2-year impact factor of 12.241 (2021), article downloads of 1,138,590 (2021), and a fast turnaround time of 11 days from submission to the first editorial decision. The journal is indexed in various databases and services, including Chemical Abstracts Service (ACS), Astrophysics Data System (ADS), Current Contents/Physical, Chemical and Earth Sciences, Journal Citation Reports/Science Edition, SCOPUS, EI Compendex, INSPEC, Google Scholar, SCImago, DOAJ, CNKI, and Science Citation Index Expanded (SCIE), among others.
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