Tipping Point for Expansion of Layered Aluminosilicates in Weakly Polar Solvents: Supercritical CO2

Layered aluminosilicates play a dominant role in the mechanical and gas storage properties of the subsurface, are used in diverse industrial applications, and serve as model materials for understanding solvent-ion-support systems. Although expansion in the presence of H2O is well-known to be systematically correlated with the hydration free energy of the interlayer cation, particularly in environments dominated by nonpolar solvents (i.e., CO2), uptake into the interlayer is not well-understood. Using novel high-pressure capabilities, we investigated the interaction of dry supercritical CO2 with Na-, NH4-, and Cs-saturated montmorillonite, comparing results with predictions from molecular dynamics simulations. Despite the known trend in H2O and that cation solvation energies in CO2 suggest a stronger interaction with Na, both the NH4- and Cs-clays readily absorbed CO2 and expanded, while the Na-clay did not. The apparent inertness of the Na-clay was not due to kinetics, as experiments seeking a stable expanded state showed that none exists. Molecular dynamics simulations revealed a large endothermicity to CO2 intercalation in the Na-clay but little or no energy barrier for the NH4- and Cs-clays. Indeed, the combination of experiment and theory clearly demonstrate that CO2 intercalation of Na-montmorillonite clays is prohibited in the absence of H2O. Consequently, we have shown for the first time that in the presence of a low dielectric constant, gas swelling depends more on the strength of the interaction between the interlayer cation and aluminosilicate sheets and less on that with solvent. The finding suggests a distinct regime in layered aluminosilicate swelling behavior triggered by low solvent polarizability, with important implications in geomechanics, storage, and retention of volatile gases, and across industrial uses in gelling, decoloring, heterogeneous catalysis, and semipermeable reactive barriers

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ANI-1, A data set of 20 million calculated off-equilibrium conformations for organic molecules

One of the grand challenges in modern theoretical chemistry is designing and implementing approximations that expedite ab initio methods without loss of accuracy. Machine learning (ML) methods are emerging as a powerful approach to constructing various forms of transferable atomistic potentials. They have been successfully applied in a variety of applications in chemistry, biology, catalysis, and solid-state physics. However, these models are heavily dependent on the quality and quantity of data used in their fitting. Fitting highly flexible ML potentials, such as neural networks, comes at a cost: a vast amount of reference data is required to properly train these models. We address this need by providing access to a large computational DFT database, which consists of more than 20 M off equilibrium conformations for 57,462 small organic molecules. We believe it will become a new standard benchmark for comparison of current and future methods in the ML potential community