Soft porous crystals have the ability to undergo large structural transformations upon exposure to external stimuli while maintaining their long-range structural order, and the size of the crystal plays an important role in this flexible behavior. Computational modeling has the potential to unravel mechanistic details of these phase transitions, provided that the models are representative for experimental crystal sizes and allow for spatially disordered phenomena to occur. Here, we take a major step forward and enable simulations of metal-organic frameworks containing more than a million atoms. This is achieved by exploiting the massive parallelism of state-of-the-art GPUs using the OpenMM software package, for which we developed a new pressure control algorithm that allows for fully anisotropic unit cell fluctuations. As a proof of concept, we study the transition mechanism in MIL-53(Al) under various external pressures. In the lower pressure regime, a layer-by-layer mechanism is observed, while at higher pressures, the transition is initiated at discrete nucleation points and temporarily induces various domains in both the open and closed pore phases. The presented workflow opens the possibility to deduce transition mechanism diagrams for soft porous crystals in terms of the crystal size and the strength of the external stimulus.
Metal–organic frameworks (MOFs) are hybrid materials constructed from metal clusters linked by organic linkers, which can be engineered for target functional applications in, for example, catalysis, sensing, and storage. The dynamic response of MOFs on external stimuli can be tuned by spatial heterogeneities such as defects and crystal size as well as by operating conditions such as temperature, pressure, moisture, and external fields. Modeling the spatiotemporal evolution of MOFs under operating conditions and at length and time scales comparable with experimental observations is extremely challenging. Herein, we give a status on the modeling of spatiotemporal processes in MOFs under working conditions and reflect on how modeling can be reconciled with in situ spectroscopy measurements.