Physics‐constrained artificial intelligence for accelerating experimental discovery of MOFs in gas separation

IF 4.4 3区工程技术 Q2 ENGINEERING, CHEMICAL

AIChE Journal Pub Date : 2026-10-01 DOI:10.1002/aic.70680

Dongming Huan, Chenkai Gu, Yawei Gu, Bingbo Shen, Rujing Hou, Jing Zhong, Yichang Pan, Weihong Xing

引用次数: 0

Abstract

Metal–organic frameworks (MOFs) hold great potential for low‐energy gas separation, but the enormous chemical space of MOFs poses a major screening challenge. Existing artificial intelligence (AI) methods suffer from high data demand and poor extrapolation capability. To address this, we develop a physics‐constrained neural network (PCNN) for propylene/propane separation that integrates physical knowledge into the model, balancing prediction accuracy and physical rationality. The PCNN achieves comparable accuracy with <10% training data of conventional models and exhibits excellent extrapolation performance. Guided by PCNN prediction, we experimentally synthesized a MOF with an ultrahigh C 3 H 6 /C 3 H 8 IAST selectivity of 3.5 × 10 5 , demonstrating the effectiveness of this method for data‐efficient MOF screening.

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加速气体分离中mof实验发现的物理约束人工智能

金属有机骨架（MOFs）在低能气体分离方面具有巨大的潜力，但MOFs巨大的化学空间给其筛选带来了重大挑战。现有人工智能（AI）方法存在数据需求大、外推能力差的问题。为了解决这个问题，我们开发了一个用于丙烯/丙烷分离的物理约束神经网络（PCNN），该网络将物理知识集成到模型中，平衡了预测准确性和物理合理性。PCNN的准确率与传统模型的训练数据<；10%相当，并具有良好的外推性能。在PCNN预测的指导下，我们实验合成了具有3.5 × 10 5的c3h6 / c3h8 IAST选择性的MOF，证明了该方法用于数据高效MOF筛选的有效性。

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

AIChE Journal 工程技术-工程：化工

CiteScore

7.10

自引率

10.80%

发文量

411

审稿时长

3.6 months

期刊介绍： The AIChE Journal is the premier research monthly in chemical engineering and related fields. This peer-reviewed and broad-based journal reports on the most important and latest technological advances in core areas of chemical engineering as well as in other relevant engineering disciplines. To keep abreast with the progressive outlook of the profession, the Journal has been expanding the scope of its editorial contents to include such fast developing areas as biotechnology, electrochemical engineering, and environmental engineering. The AIChE Journal is indeed the global communications vehicle for the world-renowned researchers to exchange top-notch research findings with one another. Subscribing to the AIChE Journal is like having immediate access to nine topical journals in the field. Articles are categorized according to the following topical areas: Biomolecular Engineering, Bioengineering, Biochemicals, Biofuels, and Food Inorganic Materials: Synthesis and Processing Particle Technology and Fluidization Process Systems Engineering Reaction Engineering, Kinetics and Catalysis Separations: Materials, Devices and Processes Soft Materials: Synthesis, Processing and Products Thermodynamics and Molecular-Scale Phenomena Transport Phenomena and Fluid Mechanics.