A comprehensive transformer-based approach for high-accuracy gas adsorption predictions in metal-organic frameworks.

ABSTRACT: Gas separation is crucial for industrial production and environmental protection, with metal-organic frameworks (MOFs) offering a promising solution due to their tunable structural properties and chemical compositions. Traditional simulation approaches, such as molecular dynamics, are complex and computationally demanding. Although feature engineering-based machine learning methods perform better, they are susceptible to overfitting because of limited labeled data. Furthermore, these methods are typically designed for single tasks, such as predicting gas adsorption capacity under specific conditions, which restricts the utilization of comprehensive datasets including all adsorption capacities. To address these challenges, we propose Uni-MOF, an innovative framework for large-scale, three-d

SUBMITTER: Wang J 

PROVIDER: S-EPMC10907743 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature