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CoeffNet: predicting activation barriers through a chemically-interpretable, equivariant and physically constrained

Sudarshan Vijay1,2, Maxwell C Venetos1,2, Evan Walter Clark Spotte-Smith1,2

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Summary
This summary is machine-generated.

CoeffNet, a new equivariant graph neural network, predicts molecular activation barriers using frontier molecular orbital coefficients. This method offers chemical interpretability and accurate predictions for reaction kinetics.

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Area of Science:

  • Computational Chemistry
  • Machine Learning in Chemistry
  • Chemical Kinetics

Background:

  • Calculating activation barriers is crucial for understanding reaction mechanisms and kinetics.
  • Traditional electronic structure methods for computing activation barriers are computationally intensive and time-consuming.
  • Identifying transition states is a bottleneck in predicting reaction rates.

Purpose of the Study:

  • Introduce CoeffNet, an equivariant graph neural network for predicting activation barriers.
  • Utilize frontier molecular orbital coefficients as graph node features for enhanced interpretability and physical constraints.
  • Demonstrate the model's capability on SN2 reactions as a proof-of-concept.

Main Methods:

  • Developed CoeffNet, an equivariant graph neural network architecture.
  • Employed coefficients of frontier molecular orbitals (e.g., highest occupied molecular orbital) from reactant and product complexes as input features.
  • Trained and validated the model on a dataset of SN2 reactions.

Main Results:

  • CoeffNet accurately predicts activation barriers with a mean absolute error below 0.025 eV.
  • The model provides chemically interpretable outputs, including transition state molecular orbital coefficients.
  • Visualization of highest occupied molecular orbital densities in transition states offers insights into reaction pathways.

Conclusions:

  • CoeffNet offers a computationally efficient and interpretable alternative for predicting activation barriers.
  • The use of molecular orbital coefficients as features enhances the physical relevance and chemical intuition of the model's predictions.
  • This approach shows significant promise for accelerating the study of molecular reaction mechanisms and kinetics.