ECC9講演: Investigation of chemical, thermal, and mechanical stability of metal-organic frameworks: an application of a universal neural network potential

The Institute of Chemistry of Irelandが主催する”9th EuChemS Chemistry Congress (ECC9)” (in Ireland) にて、講演します。

学会情報

開催期間: 2024年7月7日～7月11日
開催場所: the Convention Centre Dublin, Ireland
PFCC展示ブース: C1

プログラムセッション

日時: 2024年7月8日 10:00（現地時間）
セッション: Physical, Analytical and Computational Chemistry – Computational 1

タイトル

Investigation of chemical, thermal, and mechanical stability of metal-organic frameworks: an application of a universal neural network potential

概要

Metal-organic frameworks (MOFs) are nanoporous materials expected to be useful for many applications, including adsorption, separation, and catalysis. However, the tremendous chemical and physical diversity of metal-organic frameworks challenges accurate modeling. Preferred Potential (PFP) implemented in Matlantis is a recently developed neural network potential with its unique feature of universality even compared to other machine learning potentials. Previously, PFP was shown to perform well in predicting bulk structures of some representative MOFs.

In this paper, we present our study on the detailed examination of the chemical reactions leading to the degradation of selected MOFs using PFP. Some MOFs indicate clear degradation pathways upon exposure to humidity under specified conditions. IRMOF-1 (a.k.a MOF-5) is well-known for its sensitivity to moisture at room temperature. Molecular dynamics simulations showed that IRMOF-1 remains stable under a mildly high temperature of 400 K with an external strain. Even under the highly humid condition of 8 wt.% water, the structure remains stable at room temperature for the simulation time of 2 ns. Apparent degradation was observed when high temperature and strain were applied simultaneously under high humidity. Other selected MOFs were also tested under the same simulation conditions, and the observed stability trend is consistent with the known trend in the literature. This work demonstrates that the stability of MOFs can be tested with the capability of the universal neural network potential implemented in Matlantis. 

発表者