Accurate Rate Calculations for Hydrogen Atom Abstraction from Methane by Hydroxyl Radical: A Combination of RPMD, Machine Learning Potentials, and Active Learning

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Abstract
We employ an active learning scheme to train a neural network potential which is then utilized in RPMD simulations to calculate the thermal rate coefficients for a hydrogen atom abstraction from methane by hydroxyl radical. We use a active learning approach to determine which geometries from umbrella sampling trajectories, performed using our neural network potential, should be included in our CCSD(T) training data to improve the neural network potential. After sufficient accuracy of the neural network potential is achieved, we use this potential energy surface in RPMD trajectories to calculate the reaction rate using the Bennet-Chandler method. We find this approach to be exceptionally accurate, with errors in the rate less than 20% at 200K and decreasing to 10% at 1000K. Next, we examine the limit of how much data was required to obtain highly accurate neural network potentials and find that only 7,000 CCSD(T) are required to preserve the accuracy of the network. Finally, we examine a transfer learning approach for this reaction, and find that transfer learning does not provide sufficient accuracy for rate calculations.
Funding Information
  • National Science Foundation (CHE-1945525)
  • Camille and Henry Dreyfus Foundation (Award ML-20-146)

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