ReactionAtlas: Ab origine exploration of chemical reaction networks with machine learning
ReactionAtlas introduces a machine learning framework that autonomously builds chemical reaction networks from a small set of seed molecules without hand-crafted rules. Using a generative model and a DFT-trained machine learned force field, it discovers approximately 47,000 reactions among 12,000 compounds, achieving near-DFT accuracy for transition states. This enables new insights into prebiotic chemistry, particularly the formose cycle.
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[Submitted on 29 Jun 2026]
Title:ReactionAtlas: Ab origine exploration of chemical reaction networks with machine learning
View a PDF of the paper titled ReactionAtlas: Ab origine exploration of chemical reaction networks with machine learning, by Stefan Gugler and 3 other authors
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Abstract:Mapping a chemical reaction network, the graph of minima and transition states (TS) and the elementary reactions connecting them, is the natural language of chemistry, from catalysis to combustion to the origin of life. Constructing such a reaction network for a given chemistry has been impractical: it requires finding and characterizing tens of thousands of TS, a task for which traditional methods such as density functional theory (DFT) are typically prohibitively slow and require reactant and product as input. We introduce ReactionAtlas, which builds a reaction network $\textit{ab origine}$ from a handful of seed molecules and without hand-crafted rules. Specifically, our machine-learned generative model proposes reactions from kinetically sampled candidate compounds and a DFT-trained machine learned force field (MLFF) filters them to valid TS, the resulting products of which enter the search as new seeds. Starting from eight pre-biotic seeds (CH$_2$O, H$_2$O, OH$^-$, H$_3$O$^+$, CO$_2$, H$_2$CO$_3$, HCO$_3^-$, H), ReactionAtlas discovers $\sim$47,000 reactions among $\sim$12,000 compounds. The MLFF TSs match the PBE0 references within 0.5 Å RMSD in 85% of the cases and can be easily brought to the PBE0 level. Thus, ReactionAtlas maps small carbohydrate chemistry up to C$_4$H$_8$O$_4$ at unprecedented scale and accuracy, including charge and stereo information. It enables novel insights into many well-studied reaction paths, including the formose cycle, which we highlight for its centrality to the chemical origins of life. Notably, our framework also allows establishing alternative reaction pathways for formose chemistry.
Subjects:
Machine Learning (cs.LG); Chemical Physics (physics.chem-ph)
Cite as: arXiv:2606.30778 [cs.LG]
(or arXiv:2606.30778v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2606.30778
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Maximilian Eissler [view email] [v1] Mon, 29 Jun 2026 18:07:38 UTC (3,897 KB)
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