Pracht Philipp, Pillai Yuthika, Kapil Venkat, Csányi Gábor, Gönnheimer Nils, Vondrák Martin, Margraf Johannes T, Wales David J
Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, U.K.
Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States.
J Chem Theory Comput. 2024 Dec 24;20(24):10986-11004. doi: 10.1021/acs.jctc.4c01157. Epub 2024 Dec 12.
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 double-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 data set of organic molecules. We particularly focus on the recently reported foundational 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 high-throughput analytical workflows in chemistry.
振动光谱学是分子表征的一项基础技术，为分子材料的计算研究提供了一个理想的目标。基于之前对红外（IR）光谱有效方法的全面评估，本研究调查了气相红外光谱计算的预测准确性和计算效率，可通过现代半经验量子力学和可转移机器学习势的组合来实现。采用了一种基于双谐波近似的红外光谱预测复合方法，利用谐波振动频率与分子偶极矩的二阶导数相结合。这种方法在从分子偶极矩和相应振动模式计算红外强度时具有方法上的灵活性。系统地测试了各种方法，以提出一种合适的方案，重点是计算效率。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各种有机分子数据集上进行了评估。我们特别关注最近报道的基础机器学习势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。本研究旨在建立红外光谱高效计算预测的标准，促进未知化合物的快速可靠鉴定，并推动化学领域的自动化高通量分析工作流程。
