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[Display omitted] • Atomistic modeling techniques for microporous feature analysis. • In silico predictions of adsorption with flexible frameworks. • Data-driven methods are positioned to accelerate PIM discovery and design. Polymers of intrinsic microporosity (PIMs) are a family of materials with potential to be effective and scalable solutions for challenging adsorbent and membrane applications. The broad range of repeat unit chemistry, microporous structural features, and polymer processing makes exploration of the expansive PIM design space inefficient via chemical and materials intuition alone. Computational techniques such as molecular simulations and machine learning can provide a leap in capabilities to address this polymer design challenge and will be central to the future development of PIMs. We highlight recent microporous material studies that arrived at key results by employing computational techniques and provide our perspective on the prospects for in silico design and development of PIMs. [ABSTRACT FROM AUTHOR]

[Display omitted] • Big data approaches are needed to efficiently explore MOF catalyst space. • High-throughput protocols have been developed to screen large MOF datasets. • Machine learning is poised to accelerate MOF catalyst discovery. • While challenges remain, there are many recent developments and new opportunities. Metal-organic frameworks (MOFs) have been widely investigated for challenging catalytic transformations due to their well-defined structures and high degree of synthetic tunability. These features, at least in principle, make MOFs ideally suited for a computational approach towards catalyst design and discovery. Nonetheless, the widespread use of data science and machine learning to accelerate the discovery of MOF catalysts has yet to be substantially realized. In this review, we provide an overview of recent work that sets the stage for future high-throughput computational screening and machine learning studies involving MOF catalysts. This is followed by a discussion of several challenges currently facing the broad adoption of data-centric approaches in MOF computational catalysis, and we share possible solutions that can help propel the field forward. [ABSTRACT FROM AUTHOR]

Highlights from the article: With this special issue on the frontiers of Molecular Simulation in Chemical Engineering, we set out to highlight the broad impact that molecular modeling and simulation are making across our field. The various contributions show a plethora of methods being used in the field of molecular simulation within chemical engineering, including molecular dynamics methods, machine learning, group contribution methods and many others. Indeed, these contributions provide an excellent snapshot of the subfield of molecular simulation within chemical engineering in 2019 and should serve as a guide to newcomers and practitioners for years to come.

Diffusion of methane, ethane, propane and n-butane was studied within the micropores of several metal-organic frameworks (MOFs) of varying topologies, including the MOFs PCN-14, NU-125, NU-1100 and DUT-49. Diffusion coefficients of the pure components, as well as methane/ethane, methane/propane and methane/butane binary mixtures, were calculated using molecular dynamics simulations to understand the effect of the longer alkanes on uptake of natural gas in MOFs. The calculated self-diffusion coefficients of all four components are on the order of 10 −8 m 2 /s. The diffusion coefficients of the pure components decrease as a function of chain length in all of the MOFs studied and show different behaviour as a function of loading in different MOFs. The self-diffusivities follow the trend D PCN-14 NU-125≈NU-1100>DUT-49. By comparing the diffusion of pure methane and methane mixtures with the higher alkanes, it is observed that the diffusivity of methane is unaffected by the presence of the higher alkanes in the MOFs considered, indicating that the diffusion path of methane is not blocked by the higher alkanes present in natural gas. [ABSTRACT FROM AUTHOR]

The methane deliverable capacity of adsorbent materials is a critical performance metric that will determine the viability of using adsorbed natural gas (ANG) technology in vehicular applications. ARPA-E recently set a target deliverable capacity of 315 cc(STP)/cc that a viable adsorbent material should achieve to yield a driving range competitive with incumbent fuels. However, recent computational screening of hundreds of thousands of materials suggests that the target is unattainable. In this work, we aim to determine whether the observed limits in deliverable capacity (~200 cc(STP)/cc) are fundamental limits arising from thermodynamic or material design constraints. Our efforts focus on simulating methane adsorption isotherms in a large number of systems, resulting in a broad exploration of different combinations of spatial distributions and energetics of adsorption sites. All systems were classified into five adsorption scenarios with varying degrees of realism in the manner that adsorption sites are created and endowed with energetics. The scenarios range from methane adsorption on discrete idealized lattice sites to adsorption in metal–organic frameworks with coordinatively unsaturated sites (CUS) provided by metalated catechol groups. Our findings strongly suggest that the ARPA-E target is unattainable, although not due to thermodynamic constraints but due to material design constraints. On the other hand, we also find that the currently observed deliverable capacity limits may be moderately surpassed. For instance, incorporation of CUS in IRMOF-10 is predicted to yield a 217 cc(STP)/cc deliverable capacity. The modified material has a ~0.85 void fraction and a heat of adsorption of ~15 kJ/mol. This suggests that similar, moderate improvements over existing materials could be achieved as long as CUS incorporation still maintains a relatively large void fraction. Nonetheless, we conclude that more significant improvements in deliverable capacity will require changes in the currently proposed operation conditions. [ABSTRACT FROM AUTHOR]