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.

Modeling from Theory and Modeling from Data: Complementary or Alternative Approaches? The Case of Ionic Liquids

In the field of ionic liquids (ILs), theory-driven modeling approaches aimed at the best fit for all available data by using a unique, and often nonlinear, model have been widely adopted to develop quantitative structure-property relationship (QSPR) models. In this context, we propose chemoinformatic and chemometric data-driven procedures that lead to QSPR soft models with local validity that are able to predict relevant physicochemical properties of ILs, such as viscosity, density, decomposition temperature, and conductivity. These models, which use readily available and easily interpretable VolSurf+ descriptors, represent an unexploited opportunity for experimentalists to model and predict the physicochemical properties of ILs in industrial R&D design.