Work

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

Year: 2026

Type: article

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.

Source: AIChE Journal

DOI: 10.1002/aic.70680