A Machine-Learned Symbolic Committor for a Chemical Reaction: Isomerization in Retinal and p -Coumaric Acid

Abstract The thermal cis-trans isomerization of C═C double bonds in chromophores is a prototypical high-barrier reaction whose mechanism hinges on subtle out-of-plane bending motions. Combining machine-learning-guided path sampling (AIMMD) with symbolic regression, we learn an interpretable committor for two chemically distinct chromophores, N-retinylidene-lysine and p-coumaric acid anion, in vacuo from unbiased molecular dynamics trajectories generated by two-way shooting. Parametrizing the committor with a neural network and sampling it uniformly across the full range allows the model to resolve the reaction coordinate across the full transition region, not only at the isocommittor surface pB(x) = 0.5. A feature-attribution analysis identifies four proper dihedrals around the reactive bond as the informative coordinates, while the improper dihedrals at the sp2-hybridized carbon atoms prove unsuitable because reactant, transition, and product states share the same values. Symbolic regression then distills the committor into compact analytical expressions─to our knowledge the first committor both learned and distilled into closed form for a chemical reaction─and shows that a coupling of all four dihedrals is required to reproduce the S-shaped, stepwise pathway seen in the transition-path ensemble. This S-shape is absent from the minimum free-energy path: it arises from the dynamics of the short transition events combined with the mass asymmetry between heavy-atom and hydrogen-bearing dihedrals, and the larger asymmetry of p-coumaric acid yields the more pronounced S-shape this picture predicts. An interpretable, machine-learned committor, though a function of configuration alone, thus exposes dynamical features of the mechanism to which the free-energy surface is blind. The workflow requires no prior assumptions about the reaction coordinate and extends naturally to other high-barrier isomerizations and, combined with a reactive potential, opens a path toward bond-breaking chemical reactions.

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Journal
Journal of Chemical Theory and Computation
Published
2026-10-30
DOI
https://doi.org/10.1021/acs.jctc.6c01496
Primary Topic
Machine Learning in Materials Science
Type
article
Field-Weighted Citation Impact
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article

A Machine-Learned Symbolic Committor for a Chemical Reaction: Isomerization in Retinal and p -Coumaric Acid

Gianluca Lattanzi, Gianmarco Lazzeri, Kai Töpfer, Roberto Covino et al.
5 links
Journal of Chemical Theory and Computation
Machine Learning in Materials Science
article

A Machine-Learned Symbolic Committor for a Chemical Reaction: Isomerization in Retinal and p -Coumaric Acid

Gianluca Lattanzi, Gianmarco Lazzeri, Kai Töpfer, Roberto Covino, Bettina G. Keller, Florian Renner, Christopher J.I. Bläß, Vittoria Ossanna
article en

Abstract

Abstract The thermal cis-trans isomerization of C═C double bonds in chromophores is a prototypical high-barrier reaction whose mechanism hinges on subtle out-of-plane bending motions. Combining machine-learning-guided path sampling (AIMMD) with symbolic regression, we learn an interpretable committor for two chemically distinct chromophores, N-retinylidene-lysine and p-coumaric acid anion, in vacuo from unbiased molecular dynamics trajectories generated by two-way shooting. Parametrizing the committor with a neural network and sampling it uniformly across the full range allows the model to resolve the reaction coordinate across the full transition region, not only at the isocommittor surface pB(x) = 0.5. A feature-attribution analysis identifies four proper dihedrals around the reactive bond as the informative coordinates, while the improper dihedrals at the sp2-hybridized carbon atoms prove unsuitable because reactant, transition, and product states share the same values. Symbolic regression then distills the committor into compact analytical expressions─to our knowledge the first committor both learned and distilled into closed form for a chemical reaction─and shows that a coupling of all four dihedrals is required to reproduce the S-shaped, stepwise pathway seen in the transition-path ensemble. This S-shape is absent from the minimum free-energy path: it arises from the dynamics of the short transition events combined with the mass asymmetry between heavy-atom and hydrogen-bearing dihedrals, and the larger asymmetry of p-coumaric acid yields the more pronounced S-shape this picture predicts. An interpretable, machine-learned committor, though a function of configuration alone, thus exposes dynamical features of the mechanism to which the free-energy surface is blind. The workflow requires no prior assumptions about the reaction coordinate and extends naturally to other high-barrier isomerizations and, combined with a reactive potential, opens a path toward bond-breaking chemical reactions.

Journal of Chemical Theory and Computation
Goethe University Frankfurt (DE), University of Trento (IT), Frankfurt Institute for Advanced Studies (DE), Freie Universität Berlin (DE)
Deutsche Forschungsgemeinschaft, Volkswagen Foundation, Goethe-Universität Frankfurt am Main, Freie Universität Berlin, Johanna Quandt Stiftung, Frankfurt Institute for Advanced Studies
Openalex Percentile: Top 29%
Machine Learning in Materials Science
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