Neural Network Based ∆-Machine learning approach efficiently brings the DFT potential energy surface to the CCSD(T) quality: a case for the OH + CH3OH reaction
Abstract:The recently proposed permutationally invariant polynomial-neural network (PIP-NN) based ∆-machine learning (∆-ML) approach (PIP-NN ∆-ML, J. Phys. Chem. Lett. 2022, 13, 4729) is a flexible, general, and highly cost-efficient method to develop full dimensional accurate potential energy surface (PES). Only a small portion of points, which can be actively selected from the low-level (often DFT) points, with high-level energies are needed to bring a low-level PES to a high-level of quality. The hydrogen abstractio… Show more
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