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In many fields, from semiconductors for opto-electronic applications to ionic liquids (ILs) for separations, the glass transition temperature (Tg) of a material is a useful gauge for its potential use in practical settings. As a result, there is a great deal of interest in predicting Tg using molecular simulations. However, the uncertainty and variation in the trend shift method, a common approach in simulations to predict Tg, can be high. This is due to the need for human intervention in defining a fitting range for linear fits of density with temperature assumed for the liquid and glass phases across the simulated cooling. The definition of such fitting ranges then defines the estimate for the Tg as the intersection of linear fits. We eliminate this need for human intervention by leveraging the Shapiro–Wilk normality test and proposing an algorithm to define the fitting ranges and, consequently, Tg. Through this integration, we incorporate into our automated methodology that residuals must be normally distributed around zero for any fit, a requirement that must be met for any regression problem. Consequently, fitting ranges for realizing linear fits for each phase are statistically defined rather than visually inferred, obtaining an estimate for Tg without any human intervention. The method is also capable of finding multiple linear regimes across density vs temperature curves. We compare the predictions of our proposed method across multiple IL and semiconductor molecular dynamics simulation results from the literature and compare other proposed methods for automatically detecting Tg from density–temperature data. We believe that our proposed method would allow for more consistent predictions of Tg. We make this methodology available and open source through GitHub. [ABSTRACT FROM AUTHOR]

Soft porous coordination polymers (SPCPs) are materials with exceptional potential because of their ability to incorporate the properties of nominally rigid porous materials like metal–organic frameworks (MOFs) and those of soft matter, such as polymers of intrinsic microporosity (PIMs). This combination could offer the gas adsorption properties of MOFs together with the mechanical stability and processability of PIMs, opening up a space of flexible, highly responsive adsorbing materials. In order to understand their structure and behavior, we present a process for the construction of amorphous SPCPs from secondary building blocks. We then use classical molecular dynamics simulations to characterize the resulting structures based on branch functionalities (f), pore size distributions (PSDs), and radial distribution functions and compare them to experimentally synthesized analogs. In the course of this comparison, we demonstrate that the pore structure of SPCPs is due to both pores intrinsic to the secondary building blocks, and intercolloid spacing between colloid particles. We also illustrate the differences in nanoscale structure based on linker length and flexibility, particularly in the PSDs, finding that stiff linkers tend to produce SPCPs with larger maximum pore sizes. [ABSTRACT FROM AUTHOR]

Molecular simulation has emerged as an essential tool for modern-day research, but obtaining proper results and making reliable conclusions from simulations requires adequate sampling of the system under consideration. To this end, a variety of methods exist in the literature that can enhance sampling considerably, and increasingly sophisticated, effective algorithms continue to be developed at a rapid pace. Implementation of these techniques, however, can be challenging for experts and non-experts alike. There is a clear need for software that provides rapid, reliable, and easy access to a wide range of advanced sampling methods and that facilitates implementation of new techniques as they emerge. Here we present SSAGES, a publicly available Software Suite for Advanced General Ensemble Simulations designed to interface with multiple widely used molecular dynamics simulations packages. SSAGES allows facile application of a variety of enhanced sampling techniques—including adaptive biasing force, string methods, and forward flux sampling—that extract meaningful free energy and transition path data from all-atom and coarse-grained simulations. A noteworthy feature of SSAGES is a user-friendly framework that facilitates further development and implementation of new methods and collective variables. In this work, the use of SSAGES is illustrated in the context of simple representative applications involving distinct methods and different collective variables that are available in the current release of the suite. The code may be found at: https://github.com/MICCoM/SSAGES-public. [ABSTRACT FROM AUTHOR]

There is an almost unlimited number of metal-organic frameworks (MOFs). This creates exciting opportunities but also poses a problem: how do we quickly find the best MOFs for a given application? Molecular simulations have advanced sufficiently that many MOF properties - especially structural and gas adsorption properties - can be predicted computationally, and molecular modeling techniques are now used increasingly to guide the synthesis of new MOFs. With increasing computational power and improved simulation algorithms, it has become possible to conduct high-throughput computational screening to identify promising MOF structures and uncover structure-property relations. We review these efforts and discuss future directions in this new field. [ABSTRACT FROM AUTHOR]

Highlights: [•] MOFs with Mg alkoxide functionalizations were studied. [•] Hydrogen diffusion coefficients were obtained from MD simulations. [•] Results suggest interesting differences in diffusivities. [•] Mass transfer should be rapid in hydrogen storage applications of these materials. [ABSTRACT FROM AUTHOR]