Ab initio molecular dynamics benchmarking study of machine-learned potential energy surfaces for the HBr+ + HCl reaction

Fujioka, Kazuumi; Lam, Eric; Loi, Brandon; Sun, Rui

doi:10.1016/j.cartre.2023.100257

Cited by 1 publication

(1 citation statement)

References 62 publications

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“…When the accuracy of rate constant prediction is the goal, semiclassical or quasi-classical trajectory (QCT) calculations are typically undertaken using a system-specific potential energy surface (PES) − instead of using a classical MD force field. Trajectory based rate constant calculation packages such as DiNT, VENUS , or internal codes can be used to train these PESs and calculate the rate constants using trajectories.…”

Section: Introductionmentioning

confidence: 99%

Quasi-Classical Trajectory Calculation of Rate Constants Using an Ab Initio Trained Machine Learning Model (aML-MD) with Multifidelity Data

Shi,

Lele,

Jasper

et al. 2024

J. Phys. Chem. A

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Machine learning (ML) provides a great opportunity for the construction of models with improved accuracy in classical molecular dynamics (MD). However, the accuracy of a ML trained model is limited by the quality and quantity of the training data. Generating large sets of accurate ab initio training data can require significant computational resources. Furthermore, inconsistent or incompatible data with different accuracies obtained using different methods may lead to biased or unreliable ML models that do not accurately represent the underlying physics. Recently, transfer learning showed its potential for avoiding these problems as well as for improving the accuracy, efficiency, and generalization of ML models using multifidelity data. In this work, ab initio trained ML-based MD (aML-MD) models are developed through transfer learning using DFT and multireference data from multiple sources with varying accuracy within the Deep Potential MD framework. The accuracy of the force field is demonstrated by calculating rate constants for the H + HO 2 → H 2 + 3 O 2 reaction using quasi-classical trajectories. We show that the aML-MD model with transfer learning can accurately predict the rate constants while reducing the computational cost by more than five times compared to the use of more expensive quantum chemistry training data sets. Hence, the aML-MD model with transfer learning shows great potential in using multifidelity data to reduce the computational cost involved in generating the training set for these potentials.

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