Computational Design of Low-Volatility Lubricants for Space Using Interpretable Machine Learning

TL;DR

This paper presents an interpretable machine learning approach to predict vapor pressure of lubricants, facilitating the discovery of new low-volatility liquids suitable for space applications in mechanical assemblies.

Contribution

It introduces a data-driven ML model trained on simulations and experiments that predicts vapor pressure with interpretability, aiding virtual screening of space-compatible lubricants.

Findings

ML models accurately predict vapor pressure

Identified candidate molecules for space lubricants

Enhanced understanding of chemical structure-vapor pressure relationships

Abstract

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.