Ionic Liquid Design Using Molecular Simulation and Statistical Methods

Thesis (Ph.D.)--University of Washington, 2019Solicitous use of time is crucial for material design. In the material domain presented in this work, ionic liquids (ILs), there are theoretically 1014-18 possible pairwise molecular structures—too many to synthesize exhaustively.1,2 For this particular design problem, we turn to computational approaches. But even here an exhaustive approach is intractable. A method of deciding what system to simulate in the first place must be optimized. There are many deterministic and stochastic algorithms available for such search spaces. The Darwinian processes of evolutionary algorithms (EAs), work by mutating a candidate solution until it attains a desired fitness. In this case, the fitness is determined by a quantitative structure property relationship (QSPR) usually in the form of a machine learning (ML) model. Because the ML model is based on learning examples, it pairs well with the search strategy of an EA—starting molecular configurations for the EA are based on the same examples that have informed the ML model. When a particular solution deviates far from the training data (i.e. its molecular structure is different than the structures of the molecules in the training data) the uncertainty estimate in its property prediction is high. When this occurs, the EA solution can be simulated in MD to either: a) explore the structure landscape and inform/update the model or b) exploit the structure landscape because the prediction is close to our target. The holy grail, however, of any material design process is to operate on smooth featural or structural surfaces, and therefore be able to calculate explicit gradients to iterate toward the target property in question. Such a design process is explored through the generative capabilities of a class of stochastic neural networks—variational autoencoders—for the explicit rationalization of desired IL thermodynamic properties

Project: Genetic Algorithms with RDKit for Molecular Structure Search

Molecular search algorithms combined with QSAR models can be used to navigate structure space and design new clean energy material

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

2018 ECS Hack Week Seattle

The ECS Data Science Hack Week will take place in Seattle from May 14-19, 2018. This page will be the central repository for information before, during, and after the event