Reaction dynamics of Diels-Alder reactions from machine learned potentials

23 June 2022, Version 1
This content is a preprint and has not undergone peer review at the time of posting.


Recent advances in the development of reactive machine-learned potentials (MLPs) promise to transform reaction modelling. However, such methods have remained computationally expensive and limited to experts. Here, we employ different MLP methods (ACE, NequIP, GAP), combined with automated fitting and active learning, to study the reaction dynamics of representative Diels-Alder reactions. We demonstrate that the ACE and NequIP MLPs can consistently achieve chemical accuracy (± 1 kcal mol−1) to the ground-truth surface with only a few hundred reference calculations. These strategies are shown to enable routine ab initio-quality classical and quantum dynamics and obtain dynamical quantities such as product ratios and free energies from non-static methods. For ambimodal reactions, product distributions were found to be strongly dependent on the QM method and less so on the type of dynamics propagated.


Machine learned potentials
Diels-Alder reaction
Quantum dynamics
Free energies

Supplementary materials

Supporting Information
Computational details, including a description of the active learning strategy, parameter optimisation, and benchmark studies for representative systems

Supplementary weblinks


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