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采用 Δ-机器学习 CCSD(T)势能对托品酮及其同位素异构体的环聚合物瞬时子隧道分裂:理论与实验握手言和。

Ring-Polymer Instanton Tunneling Splittings of Tropolone and Isotopomers using a Δ-Machine Learned CCSD(T) Potential: Theory and Experiment Shake Hands.

机构信息

Department of Chemistry and Cherry L. Emerson Center for Scientific Computation, Emory University, Atlanta, Georgia 30322, United States.

Laboratory of Physical Chemistry, ETH Zürich, 8093 Zürich, Switzerland.

出版信息

J Am Chem Soc. 2023 May 3;145(17):9655-9664. doi: 10.1021/jacs.3c00769. Epub 2023 Apr 20.

Abstract

Tropolone, a 15-atom cyclic molecule, has received much interest both experimentally and theoretically due to its H-transfer tunneling dynamics. An accurate theoretical description is challenging owing to the need to develop a high-level potential energy surface (PES) and then to simulate quantum-mechanical tunneling on this PES in full dimensionality. Here, we tackle both aspects of this challenge and make detailed comparisons with experiments for numerous isotopomers. The PES, of near CCSD(T)-quality, is obtained using a Δ-machine learning approach starting from a pre-existing low-level DFT PES and corrected by a small number of approximate CCSD(T) energies obtained using the fragmentation-based molecular tailoring approach. The resulting PES is benchmarked against DF-FNO-CCSD(T) and CCSD(T)-F12 calculations. Ring-polymer instanton calculations of the splittings, obtained with the Δ-corrected PES are in good agreement with previously reported experiments and a significant improvement over those obtained using the low-level DFT PES. The instanton path includes heavy-atom tunneling effects and cuts the corner, thereby avoiding passing through the conventional saddle-point transition state. This is in contradistinction with typical approaches based on the minimum-energy reaction path. Finally, the subtle changes in the splittings for some of the heavy-atom isotopomers seen experimentally are reproduced and explained.

摘要

三酮,一个 15 个原子的环状分子,由于其 H 转移隧穿动力学,在实验和理论上都受到了广泛关注。由于需要开发高精度的势能面(PES),然后在全维尺度上模拟该 PES 中的量子隧穿,因此对其进行准确的理论描述具有挑战性。在这里,我们解决了这一挑战的两个方面,并与大量同位素的实验进行了详细比较。该接近 CCSD(T) 质量的 PES 是使用基于Δ的机器学习方法从预先存在的低水平 DFT PES 获得的,并通过使用基于碎片的分子剪裁方法获得的少量近似 CCSD(T) 能量进行修正。所得 PES 与 DF-FNO-CCSD(T) 和 CCSD(T)-F12 计算进行了基准测试。使用Δ校正的 PES 进行的环聚合物瞬时计算得到的分裂与先前报道的实验很好地吻合,并且比使用低水平 DFT PES 得到的分裂有了显著的改善。瞬时路径包括重原子隧穿效应,并切角,从而避免通过传统的鞍点过渡态。这与基于最小能量反应路径的典型方法形成对比。最后,解释并再现了实验中观察到的某些重原子同位素的分裂的微妙变化。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/86bd/10161208/e9bdc2cbb8b3/ja3c00769_0001.jpg

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