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MoSDeF, a Python Framework Enabling Large-Scale Computational Screening of Soft Matter: Application to Chemistry-Property Relationships in Lubricating Monolayer Films
journal contribution
posted on 2020-02-28, 21:32 authored by Andrew
Z. Summers, Justin B. Gilmer, Christopher R. Iacovella, Peter T. Cummings, Clare McCabeWe
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.
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terminal group characteristicslubricating monolayer filmsfunctionalized monolayer filmsscale computational screeningrecently developed pythonrdkit cheminformatics librarysoft matter systemsbased molecular simulationsoft matterpython libraryscreening datatribological effectivenesssource packageproperty relationshipsprogrammatic constructionpredictive modelsextract informationenables truedesign frameworkadditionally develop
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