Optimization of organic molecules and macromolecules using machine learning

The number of applications of data driven materials discovery is rapidly growing. The large amount of available materials characterization and computational data, combined with high level statistical algorithms, is proving to be extremely useful in developing complex predictive models. However, in the field of soft matter, which includes complex materials such as polymers, liquids, emulsions, colloids, and gels, there is a slower adoption of informatics strategies than in adjacent fields mainly due to complexity of underlying processes and plethora of processing components that dictates the properties. In this talk, I will discuss the application of machine learning (ML) technique for optimization of ligand functionalized nanoparticles (NPs) and biopolymers. In our approach we use a combination of high throughput molecular dynamics simulations and data available from the literature to train the ML model. We address the uncertainty associated with MD simulations in the development of the model. Using this approach, we were able to design novel nanoparticle ligands capable specific desired properties driven by the specific optimization function. Our methods can significantly speed up the search for a new organic monomers (complex ligands or polymers) design based on experimental, in silico and available literature data.