Alavi Seyedeh Fatemeh, Chen Yuxinxin, Hou Yi-Fan, Ge Fuchun, Zheng Peikun, Dral Pavlo O
State Key Laboratory of Physical Chemistry of Solid Surfaces, Fujian Provincial Key Laboratory of Theoretical and Computational Chemistry, Department of Chemistry, and College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China.
Institute of Physics, Faculty of Physics, Astronomy, and Informatics, Nicolaus Copernicus University in Torun, ul. Grudziądzka 5, 87-100 Torun, Poland.
J Phys Chem Lett. 2025 Jan 16;16(2):483-493. doi: 10.1021/acs.jpclett.4c03031. Epub 2025 Jan 2.
Calculating anharmonic vibrational modes of molecules for interpreting experimental spectra is one of the most interesting challenges of contemporary computational chemistry. However, the traditional QM methods are costly for this application. Machine learning techniques have emerged as a powerful tool for substituting the traditional QM methods. Universal interatomic potentials (UIPs) hold a particular promise to deliver accurate results at a fraction of the cost of the traditional QM methods, but the performance of UIPs for calculating anharmonic vibrational frequencies remains hitherto unknown. Here we show that despite a known excellent performance of the representative UIP ANI-1ccx for thermochemical properties, it fails for the anharmonic frequencies due to the original unfortunate choice of the activation function. Hence, we recommend evaluating new UIPs on anharmonic frequencies as an additional important quality test. To remedy the shortcomings of ANI-1ccx, we introduce its reformulation ANI-1ccx-gelu with the GELU activation function, which is capable of calculating IR anharmonic frequencies with reasonable accuracy (close to B3LYP/6-31G*). We also show that our new UIP can be fine-tuned to obtain very accurate anharmonic frequencies for some specific molecules but more effort is needed to improve the overall quality of UIP and its capability for fine-tuning. The new UIP will be included as part of our universal and updatable AI-enhanced QM methods (UAIQM) platform and is available together with usage and fine-tuning tutorials in open-source MLatom at https://github.com/dralgroup/mlatom. The calculations can also be performed via a web browser at https://XACScloud.com.
计算分子的非谐振动模式以解释实验光谱是当代计算化学中最有趣的挑战之一。然而，传统的量子力学方法在这种应用中成本很高。机器学习技术已成为替代传统量子力学方法的强大工具。通用原子间势（UIPs）有望以传统量子力学方法成本的一小部分提供准确结果，但UIPs在计算非谐振动频率方面的性能迄今仍未知。在这里，我们表明，尽管代表性的UIP ANI-1ccx在热化学性质方面表现出优异性能，但由于激活函数的最初不幸选择，它在非谐频率计算上失败了。因此，我们建议将新的UIPs在非谐频率上进行评估作为一项额外的重要质量测试。为了弥补ANI-1ccx的缺点，我们引入了使用GELU激活函数的重新公式化版本ANI-1ccx-gelu，它能够以合理的精度（接近B3LYP/6-31G*）计算红外非谐频率。我们还表明，我们的新UIP可以进行微调以获得某些特定分子非常准确的非谐频率，但需要更多努力来提高UIP的整体质量及其微调能力。新的UIP将作为我们通用且可更新的人工智能增强量子力学方法（UAIQM）平台的一部分，并可在https://github.com/dralgroup/mlatom的开源MLatom中与使用和微调教程一起获得。计算也可以通过网络浏览器在https://XACScloud.com上进行。
