Computational design of low-volatility lubricants for space using interpretable machine learning.

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. SCIENTIFIC CONTRIBUTION: This work develops interpretable machine learning models for vapor pressure of low volatility molecules, identifying the structural features that drive volatility. The framework is accurate in the ultra-low-volatility regime where existing methods are unreliable, enabling virtual screening of candidate space lubricants. All code and data are publicly available, and the approach generalizes to materials discovery in other extreme environments.