MoSDeF, a Python Framework Enabling Large-Scale Computational Screening of Soft Matter: Application to Chemistry-Property Relationships in Lubricating Monolayer Films.

We demonstrate how the recently developed Python-based Molecular Simulation and Design Framework (MoSDeF) can be used to perform molecular dynamics screening of functionalized monolayer films, focusing on tribological effectiveness. MoSDeF is an open-source package that allows for the programmatic construction and parametrization of soft matter systems and enables TRUE (transferable, reproducible, usable by others, and extensible) simulations. The MoSDeF-enabled screening identifies several film chemistries that simultaneously show low coefficients of friction and adhesion. We additionally develop a Python library that utilizes the RDKit cheminformatics library and the scikit-learn machine learning library that allows for the development of predictive models for the tribology of functionalized monolayer films and use this model to extract information on terminal group characteristics that most influence tribology, based on the screening data.