Continuous Molecular Representations of Ionic Liquids.

Designing new ionic liquids (ILs) is of crucial importance for various industrial applications. However, this always leads to a daunting challenge, as the number of possible combinations of cation and anion are very high and it is impossible to experimentally propose and screen a wide pool of potential candidates. However, recent applications of machine learning (ML) models have greatly improved the overall chemical discovery pipeline. In this study, we compare different generative methods for producing ionic liquids. In this comparison, we show the following: (1) when training data is scarce, a transfer learning approach can be applied to variational autoencoders (VAEs) to generate molecular structures of the target molecule type; (2) in a VAE-like structure, separate latent spaces for the cationic and anionic moieties can result in meaningful representations for their combinative, macroscopic properties; (3) interpolating between ILs with desired properties can result in a new IL with attributes similar to the two structural end points.

Fantastic Liquids and Where To Find Them: Optimizations of Discrete Chemical Space.

We present a computational adaptive learning and design strategy for ionic liquids. In this approach we show that (1) multiple cycles of chemical search via genetic algorithm (GA), property calculation with molecular dynamics, and property modeling with physiochemical descriptors and neural networks (QSPR/NN) lead to overall lower property prediction error rates compared to the original QSPR/NN models; (2) chemical similarity and kernel density estimation are a proxy for QSPR/NN error; and (3) single QSPR/NN models projected onto two-dimensional property space recreate the experimentally observed Pareto optimum frontier and, combined with the GA, lead to new structures with properties beyond the frontier.

Chain Flexibility in Self-Assembled Monolayers Affects Protein Adsorption and Surface Hydration: A Molecular Dynamics Study.

Recent developments in the antifouling properties of Self-Assembled Monolayers (SAMs) have largely focused on increasing the enthalpic association of a hydration layer along the interface of those surfaces with water. However, an entropic penalty due to chain restriction also disfavors biomolecule-surface adsorption. To isolate the effect of this entropic penalty amid changing packing densities, molecular dynamics simulations of explicitly solvated systems of lysozyme and seven monomer length oligo (ethylene glycol) (OEG) SAMs were performed. SAM surfaces were constructed at 100%, 74%, and 53% of a maximum packing (MP) density of 4.97 Å interchain spacing and the effect of chain flexibility was isolated by selectively freezing chain monomers. The rate of protein adsorption as well as the conformation and orientation of the protein upon adsorption were examined. It was found that chain spacing was a strong determinant in adsorption properties while chain flexibility played a secondary role. Of the three packing densities, 74% of MP was the most antifouling with increased antifouling behavior at moderate chain flexibility, i.e. two to four free monomer groups.