论文标题
从经典和量子构图中的通量分子内解密的高阶结构相关性
Deciphering High-order Structural Correlations within Fluxional Molecules from Classical and Quantum Configurational Entropy
论文作者
论文摘要
我们采用了k-th近邻居构型熵的估计量，以在无参数的数值方法中解码磁通分子中的复杂高阶结构相关性超出了通常的线性，双变量相关性。该确定多体相关性的基于通用的熵方案应用于质子化乙炔的复杂构型集合，这是具有大振幅运动的通量分子的原型。 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.在中等温度约为100至800 K的中等温度下，发现了一个量子到古典的跨界方案，即经典力学开始正确地描述相关性的趋势，而它甚至在质量上甚至在质量上失败了100 k以下。最后，对核的经典描述，对量化的相关性仅在量子上进行了量化，仅在温度下才能达到量子的分析。基于完全维度的神经网络电位的技术，使我们能够在本质上融合的耦合群集准确性上详尽地采样质子化乙炔的经典和量子集合。提出的对相关性的非参数分析有望通过揭示各种自由度之间的复杂耦合来补充和指导实验测量的分析，特别是多维振动光谱。
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.
