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Δ-machine learning for potential energy surfaces: A PIP approach to bring a DFT-based PES to CCSD(T) level of theory Cite as: J. Chem. Phys. 154, 051102 (2021); doi: 10.1063/5.003830
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August 2024

2024/2025

A+
Correcting ab initio-based potential energy surfaces (PESs) has been a long-standing goal of computational chemistry. Several approaches dating from 30 years ago have been suggested. In one, a correction potential is added to an existing PES, and parameters of the correction potential are optimized by matching ro-vibrational energies to the experiment.1–3 This approach relies on being able to calculate exact rovibrational energies to make the comparison with the experiment robust. Thus,...
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