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

08:00 EDT 11th April 2019 | BioPortfolio

Summary of "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 physio-chemical 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 2-dimensional property space recreate the experimentally observed Pareto optimum frontier and, combined with the GA, lead to new structures with properties beyond the frontier.

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This article was published in the following journal.

Name: Journal of chemical information and modeling
ISSN: 1549-960X
Pages:

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