Accurate Tunneling Splittings for Ever-Larger Molecules from Transfer-Learned, CCSD(T) Quality Energy Functions

TL;DR

This paper introduces a machine learning-enhanced approach combined with high-level quantum calculations to accurately predict tunneling splittings in large molecules, surpassing traditional methods and enabling analysis of systems previously considered computationally infeasible.

Contribution

It presents a transfer-learned, CCSD(T)-quality energy function framework that improves tunneling splitting predictions for large molecules using quantum tunneling calculations.

Findings

Computed tunneling splitting for tropolone matches experimental data closely.

Predicted double hydrogen transfer in a molecular dimer is within 40% of experimental value.

Method demonstrates potential for accurate predictions in larger, complex molecular systems.

Abstract

This work combines state-of-the-art machine learning techniques with highest-level electronic structure calculations and full-dimensional quantum tunneling calculations to obtain a quantitative characterization of tunneling splittings for system sizes that are currently out of reach using traditional approaches. For intramolecular hydrogen transfer in tropolone, the best computed splitting including perturbative corrections in the ring-polymer instanton calculations is 0.94 cm$^{-1}$ and compares with 0.974 cm$^{-1}$ from experiments. On the other hand, for intermolecular double hydrogen transfer in the (propiolic acid)-(formic acid) dimer, the computations yield 0.0147 cm$^{-1}$ which is larger by 40 % compared with experiment (0.0097 cm$^{-1}$) but still in much better agreement than previous attempts (0.63 cm$^{-1}$). The strategy pursued in the present work is applicable to yet larger systems and other properties of interest and provides a rational route for highest-accuracy energy functions for prediction and benchmarking electronic structure methods vis-a-vis experiments.