利用机器学习发现电喷雾推进器推进剂
Propellant Discovery For Electrospray Thrusters Using Machine Learning
AI总结：
本研究提出基于分子结构预测离子液体是否适合作电喷雾推进器推进剂的机器学习框架，构建标注数据集评估四种算法，发现SVM性能最优并筛选出193种候选推进剂，还通过SHAP分析分子描述符的影响。
摘要：
本研究提出一种机器学习框架，可根据分子结构预测物理性质未知的离子液体是否适合作为电喷雾推进器的推进剂。我们基于密度、黏度和表面张力，将离子液体标记为适合电喷雾推进器（+1）或不适合（-1），以此构建训练数据集。离子液体由通过Mordred软件包计算的分子描述符来表征。我们评估了四种机器学习算法：逻辑回归、支持向量机（SVM）、随机森林和极端梯度提升（XGBoost），其中SVM表现出最优的预测性能。SVM从物理性质未知的离子液体数据集中预测出193种候选推进剂。此外，我们采用沙普利加性解释（SHAP）来评估各个分子描述符对模型决策的影响并进行排序。
英文摘要：
This study introduces a machine learning framework to predict the suitability of ionic liquids with unknown physical properties as propellants for electrospray thrusters based on their molecular structure. We construct a training dataset by labeling ionic liquids as suitable (+1) or unsuitable (-1) for electrospray thrusters based on their density, viscosity, and surface tension. The ionic liquids are represented by their molecular descriptors calculated using the Mordred package. We evaluate four machine learning algorithms: Logistic Regression, Support Vector Machine (SVM), Random Forest, and Extreme Gradient Boosting (XGBoost), with SVM demonstrating superior predictive performance. The SVM predicts 193 candidate propellants from a dataset of ionic liquids with unknown physical properties. Further, we employ Shapley Additive Explanations (SHAP) to assess and rank the impact of individual molecular descriptors on model decisions.
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