用AI预测电推进燃料的电离与裂解特性，提升新燃料研发效率。
AI-assisted Advanced Propellant Development for Electric Propulsion
- 用化学指纹编码分子结构，结合NIST数据训练AI模型。
- 预测电离能误差仅6.87%，质量谱相似度达0.6395，78%匹配前10谱图。
- 适合燃料研发、航天推进领域研究人员快速筛选候选化合物。
本文引入人工智能算法，用于预测新型化学化合物作为电推进替代推进剂的性能，重点在于预测其电离特性与碎片化模式。化合物的化学性质和结构通过化学指纹编码，训练数据来自NIST WebBook。AI预测的电离能和最小出现能量的平均相对误差分别为6.87%和7.99%，预测离子质量的相对误差为23.89%。在电子电离产生的完整质谱预测中，预测结果的余弦相似度为0.6395，在30 Da范围内与最相似的前10个质谱匹配率达78%。
原文摘要 · Abstract (English)
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 relative error of 6.87% and 7.99%, respectively, and a predicted ion mass with a 23.89% relative error. In the cases of full mass spectra due to electron ionisation, the predictions have a cosine similarity of 0.6395 and align with the top 10 most similar mass spectra in 78% of instances within a 30 Da range.
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