Machine learning-based predictive models allow rapid and reliable prediction of material properties and facilitate innovative materials design. Base oils used in the formulation of lubricant products are complex hydrocarbons of varying sizes and structure. This study developed Gaussian process regression-based models to accurately predict the temperature-dependent density and dynamic viscosity of 305 complex hydrocarbons. In our approach, strongly correlated/collinear predictors were trimmed, important predictors were selected by least absolute shrinkage and selection operator (LASSO) regularization and prior domain knowledge, hyperparameters were systematically optimized by Bayesian optimization, and the models were interpreted. The approach provided versatile and quantitative structure-property relationship (QSPR) models with relatively simple predictors for determining the dynamic viscosity and density of complex hydrocarbons at any temperature. In addition, we developed molecular dynamics simulation-based descriptors and evaluated the feasibility and versatility of dynamic descriptors from simulations for predicting the material properties. It was found that the models developed using a comparably smaller pool of dynamic descriptors performed similarly in predicting density and viscosity to models based on many more static descriptors. The best models were shown to predict density and dynamic viscosity with coefficient of determination (R²) values of 99.6% and 97.7%, respectively, for all data sets, including a test data set of 45 molecules. Finally, partial dependency plots (PDPs), individual conditional expectation (ICE) plots, local interpretable model-agnostic explanation (LIME) values, and trimmed model R² values were used to identify the most important static and dynamic predictors of the density and viscosity.

Molecular descriptors characterize the biological, physical, and chemical properties of molecules and have long been used for understanding molecular interactions and facilitating materials design. Some of the most robust descriptors are derived from geometrical representations of molecules, called 3-dimensional (3D) descriptors. When calculated from molecular dynamics (MD) simulation trajectories, 3D descriptors can also capture the effects of operating conditions such as temperature or pressure. However, extracting 3D descriptors from MD trajectories is non-trivial, which hinders their wide use by researchers developing advanced quantitative-structure-property-relationship models using machine learning. Here, we describe a suite of open-source Python-based post-processing routines, called PyL3dMD, for calculating 3D descriptors from MD simulations. PyL3dMD is compatible with the popular simulation package LAMMPS and enables users to compute more than 2000 3D molecular descriptors from atomic trajectories generated by MD simulations. PyL3dMD is freely available via GitHub and can be easily installed and used as a highly flexible Python package on all major platforms (Windows, Linux, and macOS). A performance benchmark study used descriptors calculated by PyL3dMD to develop a neural network and the results showed that PyL3dMD is fast and efficient in calculating descriptors for large and complex molecular systems with long simulation durations. PyL3dMD facilitates the calculation of 3D molecular descriptors using MD simulations, making it a valuable tool for cheminformatics studies.

Molecular dynamics simulations were performed to study nanoscale friction on hydrophilic and hydrophobic self-assembled monolayers (SAMs) immersed in water. Sliding was simulated in two different directions to capture anisotropy due to the direction of motion relative to the inherent tilted orientation of the molecules. It was shown that friction depends on both hydrophobicity and sliding direction, with the highest friction observed for sliding on hydrophobic SAM in the direction against the initial orientation of the molecules. The origins of the friction trends were analyzed by differentiating the tip-SAM and tip-water force contributions to friction. The tip-water force was higher on the hydrophilic SAM, and this was shown to be due to the presence of a dense layer of water adjacent to the surface and hydrogen bonding. In contrast, the tip-SAM force was higher on the hydrophobic SAM due to a water depletion layer, which enabled the tip to be closer to the SAM terminal group. The higher-friction cases all exhibited greater penetration of the tip below the surface of the SAM, accommodated by further tilting and reorientation of the SAM molecules.

Abstract: Electrical contact resistance (ECR) measurements performed via conductive atomic force microscopy (C-AFM) suffer from poor reliability and reproducibility. These issues are due to a number of factors, including sample roughness, contamination via adsorbates, changes in environmental conditions such as humidity and temperature, as well as deformation of the tip apex caused by contact pressures and/or Joule heating. Consequently, ECR may vary dramatically from measurement to measurement even on a single sample tested with the same instrument. Here we present an approach aimed at improving the reliability of such measurements by addressing multiple sources of variability. In particular, we perform current-voltage spectroscopy on atomically flat terraces of highly oriented pyrolytic graphite (HOPG) under an inert nitrogen atmosphere and at controlled temperatures. The sample is annealed before the measurements to desorb adsorbates, and conductive diamond tips are used to limit tip apex deformation. These precautions lead to measured ECR values that follow a Gaussian distribution with significantly smaller standard deviation than those obtained under conventional measurement conditions. The key factor leading to this improvement is identified as the switch from ambient conditions to a dry nitrogen atmosphere. Despite these improvements, spontaneous changes in ECR are observed during measurements performed over several minutes. However, it is shown that such variations can be suppressed by applying a higher normal load.

Greased bearings in electric motors (EMs) are subject to a wide range of operational requirements and corresponding micro-environments. Consequently, greases must function effectively in these conditions. Here, the tribological performance of four market-available EM greases was characterized by measuring friction and wear of silicon nitride sliding on hardened 52100 steel. The EM greases evaluated had similar viscosity grades but different combinations of polyurea or lithium thickener with mineral or synthetic base oil. Measurements were performed at a range of temperature and surface roughness conditions to capture behavior in multiple lubrication regimes. Results enabled direct comparison of market-available products across different application-relevant metrics, and the analysis methods developed can be used as a baseline for future studies of EM grease performance.

Nanodiamonds (NDs) have potential uses in many applications, including as additives for liquid lubricants where they may be combined with more traditional chemicals, such as tricresyl phosphate (TCP), to form protective films on sliding surfaces. It has been shown that NDs can function synergistically with TCP to facilitate film formation on air baked iron. Here, reactive molecular dynamics simulations of TCP and NDs on an amorphous iron oxide surface reproduce experimental observations of the temperature at which film formation begins with NDs present and the effect of NDs on film composition. Analysis of chemical bonding in the simulations shows that the film formed in the presence of NDs comprises NDs and TCP that are both directly and indirectly bonded to the surface. Notably, the amount of phosphorus in the film, which is important for surface protection, is increased by TCP molecules that are indirectly bonded to the surface via NDs, which suggests that indirect bonding is one mechanism by which NDs facilitate film growth. The synergy of NDs and TCP has important implications for the development of NDs as emerging lubricant additives which must function with existing additives such as TCP in many applications.