AI-assisted Advanced Propellant Development for Electric Propulsion

2025/09/30 by Du, Angel Pan, Arana-Catania, Miguel, Gutiérrez, Enric Grustan
#Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #FOS: Physical sciences #Instrumentation and Methods for Astrophysics (astro-ph.IM) #Machine Learning (cs.LG) #Space Physics (physics.space-ph)

paper · doi:10.48550/arxiv.2509.26567

Abstract

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.

Citations

• Visualizing the Loss Landscape of Neural Nets
• An overview of gradient descent optimization algorithms
• Electric propulsion for satellites and spacecraft: established technologies and novel approaches
• Framewise phoneme classification with bidirectional LSTM and other neural network architectures
• Long Short-Term Memory

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