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机器学习预测的非谐频率对液态氟化氢热力学性质的影响。

The effect of machine learning predicted anharmonic frequencies on thermodynamic properties of fluid hydrogen fluoride.

作者信息

Khanifaev Jamoliddin, Schrader Tim, Perlt Eva

机构信息

Otto Schott Institute of Materials Research, Friedrich Schiller University Jena, 07743 Jena, Germany.

出版信息

J Chem Phys. 2024 Mar 28;160(12). doi: 10.1063/5.0195386.

DOI:10.1063/5.0195386
PMID:38516969
Abstract

Anharmonic effects play a crucial role in determining thermochemical properties of liquids and gases. For such extended phases, the inclusion of anharmonicity in reliable electronic structure methods is computationally extremely demanding, and hence, anharmonic effects are often lacking in thermochemical calculations. In this study, we apply the quantum cluster equilibrium method to transfer density functional theory calculations at the cluster level to the macroscopic, liquid, and gaseous phase of hydrogen fluoride. This allows us to include anharmonicity, either via vibrational self-consistent field calculations for smaller clusters or using a regression model for larger clusters. We obtain the structural composition of the fluid phases in terms of the population of different clusters as well as isobaric heat capacities as an example for thermodynamic properties. We study the role of anharmonicities for these analyses and observe that, in particular, the dominating structural motifs are rather sensitive to the anharmonicity in vibrational frequencies. The regression model proves to be a promising way to get access to anharmonic features, and the extension to more sophisticated machine-learning models is promising.

摘要

非谐效应在确定液体和气体的热化学性质方面起着至关重要的作用。对于此类扩展相,在可靠的电子结构方法中纳入非谐性在计算上要求极高,因此热化学计算中常常缺乏非谐效应。在本研究中,我们应用量子团簇平衡方法将团簇水平的转移密度泛函理论计算应用于氟化氢的宏观液相和气相。这使我们能够通过对较小团簇进行振动自洽场计算或使用较大团簇的回归模型来纳入非谐性。我们根据不同团簇的占据情况获得流体相的结构组成,并以等压热容为例研究热力学性质。我们研究了非谐性在这些分析中的作用,并观察到,特别是主导结构基序对振动频率的非谐性相当敏感。回归模型被证明是获取非谐特征的一种有前途的方法,将其扩展到更复杂的机器学习模型也很有前景。

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