Center for Nonlinear Phenomena and Complex Systems, Code Postal 231, Université Libre de Bruxelles, Boulevard du Triomphe, 1050 Brussels, Belgium.
Lebanese International University, Bekaa, Lebanon and International University of Beirut, Beirut, Lebanon.
J Chem Theory Comput. 2020 Mar 10;16(3):1681-1689. doi: 10.1021/acs.jctc.9b00964. Epub 2020 Feb 21.
Several methods are available to compute the anharmonicity in semirigid molecules. However, such methods are not yet routinely employed because of their high computational cost, especially for large molecules. The potential energy surface is required and generally approximated by a quartic force field potential based on ab initio calculation, thus limiting this approach to medium-sized molecules. We developed a new, fast, and accurate hybrid quantum mechanics/machine learning (QM/ML) approach to reduce the computational time for large systems. With this novel approach, we evaluated anharmonic frequencies of 37 molecules, thus covering a broad range of vibrational modes and chemical environments. The obtained fundamental frequencies reproduce results obtained using B2PLYP/def2tzvpp with a root-mean-square deviation (RMSD) of 21 cm and experimental results with a RMSD of 23 cm. Along with this very good accuracy, the computational time with our hybrid QM/ML approach scales linearly with , while the traditional full ab initio method scales as , where is the number of atoms.
有几种方法可用于计算半刚性分子中的非谐性。然而，由于计算成本高，这些方法尚未得到常规应用，尤其是对于大分子而言。需要使用势能面，通常根据从头计算方法用四次方力场势能来近似，因此这种方法仅限于中等大小的分子。我们开发了一种新的快速且准确的量子力学/机器学习 (QM/ML) 混合方法，以减少大规模系统的计算时间。通过这种新方法，我们评估了 37 个分子的非谐频率，从而涵盖了广泛的振动模式和化学环境。得到的基频与使用 B2PLYP/def2tzvpp 获得的结果具有 21cm 的均方根偏差 (RMSD)，与实验结果的 RMSD 为 23cm。除了非常好的准确性之外，我们的混合 QM/ML 方法的计算时间与 呈线性关系，而传统的全从头计算方法的计算时间则呈 关系，其中 是原子数。
