Multi-Scale Modeling of Physical Properties of Nanoporous Frameworks: Predicting Mechanical, Thermal and Adsorption Behavior

Nanoporous frameworks are a large and diverse family of materials, with a key role in various industrial processes and applications such as energy production and conversion, fluid separation, gas storage, water harvesting, and many more. The performance and suitability of nanoporous materials for each specific application are directly related to both its physical and chemical properties, and their determination is crucial for process engineering and optimization of performances. In this Account, we focus on some recent developments in the multi-scale modeling of physical properties of nanoporous frameworks, highlighting the latest advances in three specific areas: mechanical properties, thermal properties, and adsorption. For the study of the mechanical behavior of nanoporous materials, the last few years have seen a rapid acceleration of research. For example, computational resources have been pooled to created public large-scale databases of elastic constants: those can serve as a basis for data-based discovery of materials with targeted properties, as well as the training of machine learning predictor models. The large-scale prediction of thermal behavior, in comparison, is not yet routinely performed at such large scale. Tentative databases have been assembled at the DFT level on specific families of materials, like zeolites, but prediction at larger scale currently requires the use of transferable, classical force fields, whose accuracy can be limited. Finally, adsorption is naturally one of the most studied physical properties of nanoporous frameworks, as fluid separation or storage is often the primary target for these materials. We highlight the recent achievements and open challenges for adsorption prediction at large scale, focusing in particular on the accuracy of computational models and the reliability of comparisons with experimental data available. We detail some recent methodological improvements in the prediction of adsorption-related properties, including thermodynamic quantities and transport properties. Finally, we stress the importance for data-based methods of addressing all sources of uncertainty. The Account concludes with some perspectives about the latest developments and open questions in data-based approaches, and the integration of computational and experimental data together in the materials discovery loop.