用可解释机器学习设计低挥发性太空润滑剂，加速新材料发现。
Computational Design of Low-Volatility Lubricants for Space Using Interpretable Machine Learning
- 结合分子模拟与实验数据训练可解释模型，预测润滑剂挥发性。
- 识别出数种低挥发性候选分子，适合作为未来太空机械部件润滑剂。
- 模型可揭示化学结构与挥发性的关系，指导理性设计。
空间中运动机械组件（MMAs）的性能和寿命取决于润滑剂的特性。高速或高循环工况需使用液体润滑剂，因其能重新流至接触点。然而，目前仅有少数液体润滑剂在真空环境下挥发性足够低，且各自存在局限性，限制了MMAs的设计。本文提出一种数据驱动的机器学习方法，用于预测液体润滑剂的蒸气压，实现虚拟筛选与新润滑剂的发现。模型基于高通量分子动力学模拟与实验数据库进行训练，强调可解释性，从而揭示化学结构与蒸气压之间的关联。基于此，提出若干具有潜力的候选分子，可用于未来太空机械系统中的液体润滑剂。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。