AI-assisted Advanced Propellant Development for Electric Propulsion
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866908569284116480 |
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| author | Du, Angel Pan Arana-Catania, Miguel Gutiérrez, Enric Grustan |
| author_facet | Du, Angel Pan Arana-Catania, Miguel Gutiérrez, Enric Grustan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_26567 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | AI-assisted Advanced Propellant Development for Electric Propulsion Du, Angel Pan Arana-Catania, Miguel Gutiérrez, Enric Grustan Instrumentation and Methods for Astrophysics Artificial Intelligence Machine Learning Space Physics 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. |
| title | AI-assisted Advanced Propellant Development for Electric Propulsion |
| topic | Instrumentation and Methods for Astrophysics Artificial Intelligence Machine Learning Space Physics |
| url | https://arxiv.org/abs/2509.26567 |