AI-assisted Advanced Propellant Development for Electric Propulsion
Angel Pan Du, Miguel Arana-Catania, Enric Grustan Gutiérrez
人工智能算法作为预测新化合物作为电力推进的替代推进剂的性能的工具,专注于预测它们的电离特性和碎片模式。 化合物的化学性质和结构使用化学指纹进行编码,训练数据集从NIST WebBook中提取。 AI预测的电离能量和最小外观能量的平均相对误差为6.87
Artificial Intelligence algorithms are introduced in this work as a tool to predict the performance of new chemical compounds as alternative propellants for electric propulsion, focusing on predicting their ionisation characteristics and fragmentation patterns. The chemical properties and structure of the compounds are encoded using a chemical fingerprint, and the training datasets are extracted from the NIST WebBook. The AI-predicted ionisation energy and minimum appearance energy have a mean r...