Machine learning using host/guest energy histograms to predict adsorption in metal-organic frameworks: Application to short alkanes and Xe/Kr mixtures.

A machine learning (ML) methodology that uses a histogram of interaction energies has been applied to predict gas adsorption in metal-organic frameworks (MOFs) using results from atomistic grand canonical Monte Carlo (GCMC) simulations as training and test data. In this work, the method is first extended to binary mixtures of spherical species, in particular, Xe and Kr. In addition, it is shown that single-component adsorption of ethane and propane can be predicted in good agreement with GCMC simulation using a histogram of the adsorption energies felt by a methyl probe in conjunction with the random forest ML method. The results for propane can be improved by including a small number of MOF textural properties as descriptors. We also discuss the most significant features, which provides physical insight into the most beneficial adsorption energy sites for a given application.

Adsorption and Diffusion of Fluids in Defective Carbon Nanotubes: Insights from Molecular Simulations.

Single-walled carbon nanotubes (SWNTs) have been shown from both simulations and experiments to have remarkably low resistance to gas and liquid transport. This has been attributed to the remarkably smooth interior surface of pristine SWNTs. However, real SWNTs are known to have various defects that depend on the synthesis method and procedure used to activate the SWNTs. In this paper, we study adsorption and transport properties of atomic and molecular fluids in SWNTs having vacancy point defects. We construct models of defective nanotubes that have either unrelaxed defects, where the overall structure of the SWNT is not changed, or reconstructed defects, where the bonding topology and therefore the shape of the SWNT is allowed to change. Furthermore, we include partial atomic charges on the SWNT carbon atoms due to the reconstructed defects. We consider adsorption and diffusion of Ar atoms and CO2 and H2O molecules as examples of a noble gas, a linear quadrupolar fluid, and a polar fluid. Adsorption isotherms were found to be fairly insensitive to the defects, even for the case of water in the charged, reconstructed SWNT. We have computed both the self-diffusivities and corrected diffusivities (which are directly related to the transport diffusivities) for each of these fluids. In general, we found that at zero loading that defects can dramatically reduce the self- and corrected diffusivities. However, at high, liquidlike loadings, the self-diffusion coefficients for pristine and defective nanotubes are very similar, indicating that fluid-fluid collisions dominate the dynamics over the fluid-SWNT collisions. In contrast, the corrected diffusion coefficients can be more than an order of magnitude lower for water in defective SWNTs. This dramatic decrease in the transport diffusion is due to the formation of an ordered structure of water, which forms around a local defect site. It is therefore important to properly characterize the level and types of defects when accurate transport diffusivities are needed.

Using mesoscopic models to design strong and tough biomimetic polymer networks.

Using computational modeling, we investigate the mechanical properties of polymeric materials composed of coiled chains, or "globules", which encompass a folded secondary structure and are cross-linked by labile bonds to form a macroscopic network. In the presence of an applied force, the globules can unfold into linear chains and thereby dissipate energy as the network is deformed; the latter attribute can contribute to the toughness of the material. Our goal is to determine how to tailor the labile intra- and intermolecular bonds within the network to produce material exhibiting both toughness and strength. Herein, we use the lattice spring model (LSM) to simulate the globules and the cross-linked network. We also utilize our modified Hierarchical Bell model (MHBM) to simulate the rupture and reforming of N parallel bonds. By applying a tensile deformation, we demonstrate that the mechanical properties of the system are sensitive to the values of N(in) and N(out), the respective values of N for the intra- and intermolecular bonds. We find that the strength of the material is mainly controlled by the value of N(out), with the higher value of N(out) providing a stronger material. We also find that, if N(in) is smaller than N(out), the globules can unfold under the tensile load before the sample fractures and, in this manner, can increase the ductility of the sample. Our results provide effective strategies for exploiting relatively weak, labile interactions (e.g., hydrogen bonding or the thiol/disulfide exchange reaction) in both the intra- and intermolecular bonds to tailor the macroscopic performance of the materials.