TY - JOUR
T1 - MoSDeF, a Python Framework Enabling Large-Scale Computational Screening of Soft Matter
T2 - Application to Chemistry-Property Relationships in Lubricating Monolayer Films
AU - Summers, Andrew Z.
AU - Gilmer, Justin B.
AU - Iacovella, Christopher R.
AU - Cummings, Peter T.
AU - Mccabe, Clare
N1 - Funding Information:
Funding for this work has been provided by the National Science Foundation (NSF) through Grants ACI-1047827, OAC-1835874, and OAC-1535150.
Funding Information:
The authors would like to thank Simon Adorf for assistance in onboarding with the Signac framework. We would additionally like to acknowledge Christoph Klein and Janos Sallai for their work on the mBuild and Foyer packages facilitating this work. An award of computer time was provided by the INCITE program. This research used resources of the Oak Ridge Leadership Computing Facility, which is a DOE Office of Science User Facility supported under Contract DE-AC05-00OR22725. All simulations were performed on the Titan supercomputer at the Oak Ridge Leadership Computing Facility.
Publisher Copyright:
Copyright © 2020 American Chemical Society.
PY - 2020/3/10
Y1 - 2020/3/10
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85081912622&partnerID=8YFLogxK
U2 - 10.1021/acs.jctc.9b01183
DO - 10.1021/acs.jctc.9b01183
M3 - Article
C2 - 32004433
AN - SCOPUS:85081912622
SN - 1549-9618
VL - 16
SP - 1779
EP - 1793
JO - Journal of Chemical Theory and Computation
JF - Journal of Chemical Theory and Computation
IS - 3
ER -