期刊
JOURNAL OF PHYSICAL CHEMISTRY LETTERS
卷 12, 期 20, 页码 4902-4909出版社
AMER CHEMICAL SOC
DOI: 10.1021/acs.jpclett.1c01142
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资金
- ARO, DURIP grant [W911NF-14-1-0471]
- NASA [80NSSC20K0360]
The study represents a significant advancement in applying Delta machine learning method to the challenging case of acetylacetone, successfully deriving a new potential energy surface which shows a barrier height close to the benchmark value.
Machine-learned potential energy surfaces (PESs) for molecules with more than 10 atoms are typically forced to use lower-level electronic structure methods such as density functional theory (DFT) and second-order Moller-Plesset perturbation theory (MP2). While these are efficient and realistic, they fall short of the accuracy of the gold standard coupled-cluster method, especially with respect to reaction and isomerization barriers. We report a major step forward in applying a Delta-machine learning method to the challenging case of acetylacetone, whose MP2 barrier height for H-atom transfer is low by roughly 1.1 kcal/mol relative to the benchmark CCSD(T) barrier of 3.2 kcal/mol. From a database of 2151 local CCSD(T) energies and training with as few as 430 energies, we obtain a new PES with a barrier of 3.5 kcal/mol in agreement with the LCCSD(T) barrier of 3.5 kcal/mol and close to the benchmark value. Tunneling splittings due to H-atom transfer are calculated using this new PES, providing improved estimates over previous ones obtained using an MP2-based PES.
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