| 초록 |
Metal-organic frameworks (MOFs) are a class of nanoporous materials assembled from metal clusters and organic ligands. Due to their large internal surface area and tunability, many studies were performed to find well-performing structures for various applications. Recently, Molecular simulations have become an important tool to quickly determine high-performing MOFs. Prediction of adsorption performance depends critically on the molecular model, however, high-throughput computational screening relies on using the off-the-shelf forcefields that has not been optimized for this purpose. In this work, we evaluated the reliability of the forcefields on predicting the ranking from a list of materials. Our results show that the forcefields do not accurately predict the relative ranking of these materials. To improve the prediction, we developed a new forcefield for methane adsorption that can improve the prediction of relative ranking using machine learning methods, and metaheuristics. |
