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<p>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. The dataset is extremely imbalanced due to the scarcity of suitable candidates. To mitigate the issue of imbalanced data, we applied a combination of oversampling the minority class and undersampling the majority class. 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.</p>

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Discovery of ionic liquid propellants for electrospray thrusters using molecular descriptors and machine learning

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年5月30日

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  • Rafid Bendimerad,
  • Elaine Petro

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. The dataset is extremely imbalanced due to the scarcity of suitable candidates. To mitigate the issue of imbalanced data, we applied a combination of oversampling the minority class and undersampling the majority class. 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.