Deciphering High-order Structural Correlations within Fluxional Molecules from Classical and Quantum Configurational Entropy

TL;DR

This study uses entropy estimation to analyze complex many-body correlations in fluxional molecules, revealing how quantum effects influence these correlations across different temperatures, with implications for spectroscopic analysis.

Contribution

Introduces a parameter-free entropy-based method to decode high-order correlations in fluxional molecules, incorporating quantum effects with neural network potentials.

Findings

High-order correlations are crucial for understanding fluxional molecules.

Quantum delocalization reduces correlations at ultra-low temperatures.

Classical mechanics approximates quantum correlations only above 1000 K.

Abstract

We employ the k-th nearest-neighbor estimator of configurational entropy in order to decode within a parameter-free numerical approach the complex high-order structural correlations in fluxional molecules going beyond the usual linear, bivariate correlations. This generic entropy-based scheme for determining many-body correlations is applied to the complex configurational ensemble of protonated acetylene, a prototype for fluxional molecules featuring large-amplitude motion. After revealing the importance of high-order correlations beyond the simple two-coordinate picture for this molecule, we analyze in detail the evolution of the relevant correlations with temperature as well as the impact of nuclear quantum effects down to the ultra-low temperature regime of 1 K. We find that quantum delocalization and zero-point vibrations significantly reduce all correlations in protonated acetylene in the deep quantum regime. At intermediate temperatures of approximately 100 to 800 K, a quantum-to-classical cross-over regime is found where classical mechanics starts to correctly describe trends in the correlations whereas it even qualitatively fails below 100 K. Finally, a classical description of the nuclei provides correlations that are in quantitative agreement with the quantum ones only at temperatures exceeding 1000 K. This data-intensive analysis has been made possible due to recent developments of machine learning techniques based on neural network potentials in full dimensionality that allow us to exhaustively sample both, the classical and quantum ensemble of protonated acetylene at essentially converged coupled cluster accuracy. The presented non-parametric analysis of correlations is expected to complement and guide the analysis of experimental measurements, in particular multi-dimensional vibrational spectroscopy, by revealing the complex coupling between various degrees of freedom.