高效复合红外光谱：结合双谐近似与机器学习势
Efficient Composite Infrared Spectroscopy: Combining the Doubly-Harmonic Approximation with Machine Learning Potentials
AI总结：
本研究提出一种结合双谐近似与机器学习势的复合方法，用于高效准确预测气相红外光谱，并系统评估多种方法，以建立标准流程，促进未知化合物快速识别和自动化分析。
摘要：
振动光谱是分子表征的基石技术，为分子材料的计算研究提供了理想目标。在先前对红外（IR）光谱高效方法的全面评估基础上，本研究探讨了通过结合现代半经验量子力学和可迁移机器学习势所实现的气相红外光谱计算的预测准确性和计算效率。采用了一种基于双谐近似的复合方法进行红外光谱预测，该方法利用谐波振动频率与分子偶极矩平方导数的组合。这种方法允许在计算红外强度时，对分子偶极和相应振动模式进行方法上的灵活选择。系统测试了多种方法，以提出一个强调计算效率的合适方案。在这些方法中，对半经验扩展紧束缚（xTB）模型、经典电荷平衡模型以及为偶极矩预测训练的机器学习势，在一组多样化的有机分子数据集上进行了评估。我们特别关注最近报道的机器学习势MACE-OFF23，以解决传统低成本量子力学和力场方法的准确性限制。本研究旨在为高效计算预测红外光谱建立标准，促进未知化合物的快速可靠识别，并推进化学中的自动化分析工作流程。
英文摘要：
Vibrational spectroscopy is a cornerstone technique for molecular characterization and offers an ideal target for the computational investigation of molecular materials. Building on previous comprehensive assessments of efficient methods for infrared (IR) spectroscopy, this study investigates the predictive accuracy and computational efficiency of gas-phase IR spectra calculations, accessible through a combination of modern semiempirical quantum mechanical and transferable machine learning potentials. A composite approach for IR spectra prediction based on the doubly-harmonic approximation, utilizing harmonic vibrational frequencies in combination squared derivatives of the molecular dipole moment, is employed. This approach allows for methodical flexibility in the calculation of IR intensities from molecular dipoles and the corresponding vibrational modes. Various methods are systematically tested to suggest a suitable protocol with an emphasis on computational efficiency. Among these methods, semiempirical extended tight-binding (xTB) models, classical charge equilibrium models, and machine learning potentials trained for dipole moment prediction are assessed across a diverse dataset of organic molecules. We particularly focus on the recently reported machine learning potential MACE-OFF23 to address the accuracy limitations of conventional low-cost quantum mechanical and force-field methods. This study aims to establish a standard for the efficient computational prediction of IR spectra, facilitating the rapid and reliable identification of unknown compounds and advancing automated analytical workflows in chemistry.
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