Abstract
Monolayer films have shown promise as a lubricating layer to reduce friction and wear of mechanical devices with separations on the nanoscale. These films have a vast design space with many tunable properties that can affect their tribological effectiveness. For example, terminal group chemistry, film composition, and backbone chemistry can all lead to films with significantly different tribological properties. This design space, however, is very difficult to explore without a combinatorial approach and an automatable, reproducible, and extensible workflow to screen for promising candidate films. Using the Molecular Simulation Design Framework (MoSDeF), a combinatorial screening study was performed to explore 9747 unique monolayer films (116964 total simulations) and a machine learning (ML) model using a random forest regressor, an ensemble learning technique, to explore the role of terminal group chemistry and its effect on tribological effectiveness. The most promising films were found to contain small terminal groups such as cyano and ethylene. The ML model was subsequently applied to screen terminal group candidates identified from the ChEMBL small molecule library. Approximately 193131 unique film candidates were screened with approximately a five order of magnitude speed-up in analysis compared to simulation alone. The ML model was thus able to be used as a predictive tool to greatly speed up the initial screening of promising candidate films for future simulation studies, suggesting that computational screening in combination with ML can greatly increase the throughput in combinatorial approaches to generate in silico data and then train ML models in a controlled, self-consistent fashion.
Highlights
We developed a screening framework to explore the role of terminal group chemistry on thin film tribological response under shear, enabling computational screening studies to be performed over a multitude of terminal group chemistries for contacting monolayers undergoing shear using non-equilibrium molecular dynamics (NEMD) simulations.[8]
Considering first the results of the high throughput screening MD simulations, including those performed in the current study and in Summers et al.,[8] we identify 22 monolayer designs that provide favorable frictional properties, e.g., those that have low simulated coefficient of friction (COF) and F0 values
In general, these designs are in agreement with the conclusion obtained by Summers et al from a study of a considerably smaller dataset where it was noted that the COF of monolayers is primarily affected by the shape and size of the terminal group, with chemistries of small sizes and simple shapes exhibiting the lowest COF.[8]
Summary
Monolayer films have shown promise as a means of reducing friction and wear of mechanical devices with nanoscale surface separations (e.g., nano- and micro-electromechanical systems, NEMS and MEMS).[1,2] Such films are highly tunable through modification of their terminal group chemistries, backbone chain length, backbone chemistry, and film composition, all of which have been demonstrated to impact their tribological effectiveness along with other properties, such as durability, solvent interactions, and thermal response.[1,2,3,4,5] This tunability presents a rich chemical parameter space that can be explored for the optimization of film properties as well as gleaning useful information about the quantitative structure-property relationships (QSPR) of these systems, to develop better predictive models for design considerations.[6,7] Of these various modifications, the chemical and physical characteristics (descriptors) of the terminal groups plays a dominant role in the tribological response. There are a multitude of complex relationships between these various chemical/molecular descriptors when translated to monolayer polymer systems as hinted at by Le et al.[6]
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