Breaking the Coupled Cluster Barrier for Machine Learned Potentials of Large Molecules: The Case of 15-atom Acetylacetone
Chen Qu,
Paul Houston,
Riccardo Conte
et al.
Abstract: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 and second-order Møller-Plesset perturbation theory (MP2). While these are efficient and realistic, they fall short of the accuracy of the "gold standard" coupled-cluster method [CCSD(T)], especially with respect to reaction and isomerization barriers. We report a major step forward in using a ∆-machine learning method to t… Show more
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