Machine-learning to predict anharmonic frequencies: a study of models and transferability,Physical Chemistry Chemical Physics

当前位置： X-MOL 学术 › Phys. Chem. Chem. Phys. › 论文详情

Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)

Machine-learning to predict anharmonic frequencies: a study of models and transferability
Physical Chemistry Chemical Physics ( IF 2.9 ) Pub Date : 2024-08-26 , DOI: 10.1039/d4cp01789g
Jamoliddin Khanifaev ₁ , Tim Schrader ₁ , Eva Perlt ₁

Affiliation

With more and more accurate electronic structure methods at hand, the inclusion of anharmonic effects in the post-processing of such data towards thermochemical properties is the next step. In this context, the description of anharmonicity has been an important topic of physical chemistry and chemical physics for a long time. In this study, anharmonic frequencies of various hydrogen–halides and halogenated hydrocarbon molecular clusters are calculated using harmonic as well as explicitly anharmonic methods, i.e., normal mode analysis and vibrational self-consistent field. Simple harmonic model based descriptors were used to predict anharmonic frequencies via multilinear regression and gradient boosting regression. Gradient boosting regression is capable of predicting reliable anharmonic data and even the simple multilinear regression model yields reasonable predictions that can account for mode-to-mode couplings. Moreover, the transferability to unseen chemical systems is assessed and it is confirmed that the machine-learned models can be applied to larger, unseen molecules.

中文翻译：

机器学习预测非谐波频率：模型和可传递性研究

随着越来越精确的电子结构方法的出现，下一步是在此类数据的后处理中加入非谐效应以实现热化学性质。在此背景下，非调和性的描述长期以来一直是物理化学和化学物理的重要课题。在这项研究中，各种卤化氢和卤代烃分子簇的非简谐频率是使用简谐和显式非简谐方法（即简正模态分析和振动自洽场）计算的。基于简单谐波模型的描述符用于通过多元线性回归和梯度增强回归来预测非谐波频率。梯度增强回归能够预测可靠的非谐波数据，甚至简单的多线性回归模型也能产生合理的预测，可以解释模式到模式的耦合。此外，还评估了对看不见的化学系统的可转移性，并证实机器学习模型可以应用于更大的、看不见的分子。

更新日期：2024-08-26

点击分享查看原文

点击收藏

公开下载

阅读更多本刊新发论文本刊介绍/投稿指南