利用机器学习实现完全第一性原理的表面光谱学
Fully First-Principles Surface Spectroscopy with Machine Learning
AI总结：
本文结合高维神经网络原子间势与对称自适应高斯过程回归，提出以从头算精度模拟水/空气界面VSFG光谱的第一性原理机器学习方案。
摘要：
过去几十年来，由于振动和频生成（VSFG）等表面特异性光谱技术的持续发展，我们目前在分子水平上对含水界面结构与动力学的理解已大幅提升。与其他光谱技术中的情况类似，为了提取VSFG光谱中编码的全部信息，我们必须借助原子尺度模拟。后者传统上要么基于经验力场模型，这类模型无法描述化学键的断裂与形成，也无法描述具有复杂电子结构的体系；要么基于从头算计算，而这类计算因其计算成本难以实现统计收敛。这些限制最终妨碍了我们对含水界面的理解。在本工作中，我们通过结合两种机器学习技术克服了这些约束，即高维神经网络原子间势和对称自适应高斯过程回归，以从头算精度模拟水/空气界面的SFG光谱。利用原子环境的数据驱动局部分解，我们开发了一种简单方案，使我们能够获得与当前实验相符的VSFG光谱。此外，我们识别了不准确的主要来源，并为复杂界面的表面敏感光谱建模确立了一条清晰路径。
英文摘要：
Our current understanding of the structure and dynamics of aqueous interfaces at the molecular level has grown substantially in the last few decades due to the continuous development of surface-specific spectroscopies, such as vibrational sum-frequency generation (VSFG). Similarly to what happens in other spectroscopies, to extract all of the information encoded in the VSFG spectra we must turn to atomistic simulations. The latter are conventionally based either on empirical force field models, which cannot describe bond breaking and formation or systems with a complex electronic structure, or on \textit{ab initio} calculations which are difficult to statistically converge due to their computational cost. These limitations ultimately hamper our understanding of aqueous interfaces. In this work, we overcome these constraints by combining two machine learning techniques, namely high-dimensional neural network interatomic potentials and symmetry-adapted Gaussian process regression, to simulate the SFG spectra of the water/air interface with \textit{ab initio} accuracy. Leveraging a data-driven local decomposition of atomic environments, we develop a simple scheme that allows us to obtain VSFG spectra in agreement with current experiments. Moreover, we identify the main sources of inaccuracy and establish a clear pathway towards the modelling of surface-sensitive spectroscopy of complex interfaces.
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