Data-driven discovery of chemical reaction networks
Fuente:
arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866910020208164864 |
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| author | Reyes-Velazquez, Abraham Güttel, Stefan Larrosa, Igor Latz, Jonas |
| author_facet | Reyes-Velazquez, Abraham Güttel, Stefan Larrosa, Igor Latz, Jonas |
| contents | We propose a unified framework that allows for the full mechanistic reconstruction of chemical reaction networks (CRNs) from concentration data. The framework utilizes an integral formulation of the differential equations governing the chemical reactions, followed by an automatic procedure to recover admissible mass-action mechanisms from the equations. We provide theoretical justification for the use of integral formulations using analytical and numerical error bounds. The integral formulation is demonstrated to offer superior robustness to noise and improved accuracy in both rate-law and graph recovery when compared to other commonly used formulations. Together, our developments advance the goal of fully automated, data-driven chemical mechanism discovery. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_11849 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Data-driven discovery of chemical reaction networks Reyes-Velazquez, Abraham Güttel, Stefan Larrosa, Igor Latz, Jonas Numerical Analysis We propose a unified framework that allows for the full mechanistic reconstruction of chemical reaction networks (CRNs) from concentration data. The framework utilizes an integral formulation of the differential equations governing the chemical reactions, followed by an automatic procedure to recover admissible mass-action mechanisms from the equations. We provide theoretical justification for the use of integral formulations using analytical and numerical error bounds. The integral formulation is demonstrated to offer superior robustness to noise and improved accuracy in both rate-law and graph recovery when compared to other commonly used formulations. Together, our developments advance the goal of fully automated, data-driven chemical mechanism discovery. |
| title | Data-driven discovery of chemical reaction networks |
| topic | Numerical Analysis |
| url | https://arxiv.org/abs/2602.11849 |