Litman Yair, Lan Jinggang, Nagata Yuki, Wilkins David M
Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, U.K.
Max Planck Institute for Polymer Research, Ackermannweg 10, 55128 Mainz, Germany.
J Phys Chem Lett. 2023 Sep 14;14(36):8175-8182. doi: 10.1021/acs.jpclett.3c01989. Epub 2023 Sep 6.
Our current understanding of the structure and dynamics of aqueous interfaces at the molecular level has grown substantially due to the continuous development of surface-specific spectroscopies, such as vibrational sum-frequency generation (VSFG). As in other vibrational spectroscopies, we must turn to atomistic simulations to extract all of the information encoded in the VSFG spectra. The high computational cost associated with existing methods means that they have limitations in representing systems with complex electronic structure or in achieving statistical convergence. In this work, we combine high-dimensional neural network interatomic potentials and symmetry-adapted Gaussian process regression to overcome these constraints. We show that it is possible to model VSFG signals with fully accuracy using machine learning and illustrate the versatility of our approach on the water/air interface. Our strategy allows us to identify the main sources of theoretical inaccuracy and establish a clear pathway toward the modeling of surface-sensitive spectroscopy of complex interfaces.
由于表面特异性光谱学（如振动和频产生光谱，VSFG）的不断发展，我们目前在分子水平上对水相界面的结构和动力学的理解有了显著提升。与其他振动光谱学一样，我们必须借助原子模拟来提取VSFG光谱中编码的所有信息。现有方法的高计算成本意味着它们在表示具有复杂电子结构的系统或实现统计收敛方面存在局限性。在这项工作中，我们结合了高维神经网络原子间势和对称适应高斯过程回归来克服这些限制。我们表明，使用机器学习可以完全准确地模拟VSFG信号，并在水/空气界面上展示了我们方法的通用性。我们的策略使我们能够识别理论不准确的主要来源，并为复杂界面的表面敏感光谱建模建立一条清晰的途径。
