大分子机器学习势的耦合团簇势垒破缺：15原子乙酰丙酮的情况
Breaking the Coupled Cluster Barrier for Machine Learned Potentials of Large Molecules: The Case of 15-atom Acetylacetone
摘要：
机器学习的势能面（PES）的分子超过10个原子通常被迫使用较低水平的电子结构方法，如密度泛函理论和二阶Moller-Plesset微扰理论（MP2）。虽然这些方法是有效和现实的，但它们达不到"黄金标准“耦合簇方法的准确性，特别是在反应和异构化障碍方面。我们报告了将$Δ$ -机器学习方法应用于乙酰丙酮的挑战性情况的重要一步，其H原子转移的MP2势垒高度相对于基准CCSD（T）势垒3.2 kcal/mol低约1.5 kcal/mol。从2151个局部CCSD（T）能量的数据库中，并使用少至430个能量进行训练，我们获得了一个新的PES，其势垒为3.49 kcal/mol，与LCCSD（T）的3.54 kcal/mol一致，接近基准值。由于H-原子转移的Tunnel分裂计算使用这种新的PES，提供改进的估计比以前的使用MP2为基础的PES。
英文摘要：
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 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 $Δ$-machine learning method to the challenging case of acetylacetone, whose MP2 barrier height for H-atom transfer is low by roughly 1.5 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 3.49 kcal/mol in agreement with the LCCSD(T) one of 3.54 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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