Computational Design of Low-Volatility Lubricants for Space Using Interpretable Machine Learning
This work addresses the challenge of limited liquid lubricant options for moving mechanical assemblies in space, offering a data-driven method to discover new candidates, though it appears incremental as it builds on existing simulation and database data.
The authors tackled the problem of designing low-volatility lubricants for space applications by developing an interpretable machine learning approach to predict vapor pressure, enabling the virtual screening and proposal of several candidate molecules for future use.
The function and lifetime of moving mechanical assemblies (MMAs) in space depend on the properties of lubricants. MMAs that experience high speeds or high cycles require liquid based lubricants due to their ability to reflow to the point of contact. However, only a few liquid-based lubricants have vapor pressures low enough for the vacuum conditions of space, each of which has limitations that add constraints to MMA designs. This work introduces a data-driven machine learning (ML) approach to predicting vapor pressure, enabling virtual screening and discovery of new space-suitable liquid lubricants. The ML models are trained with data from both high-throughput molecular dynamics simulations and experimental databases. The models are designed to prioritize interpretability, enabling the relationships between chemical structure and vapor pressure to be identified. Based on these insights, several candidate molecules are proposed that may have promise for future space lubricant applications in MMAs.