Lan Jinggang, Kapil Venkat, Gasparotto Piero, Ceriotti Michele, Iannuzzi Marcella, Rybkin Vladimir V
Department of Chemistry, University of Zurich, Zürich, Switzerland.
Laboratory of Computational Science and Modelling, Institute of Materials, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland.
Nat Commun. 2021 Feb 3;12(1):766. doi: 10.1038/s41467-021-20914-0.
The nature of the bulk hydrated electron has been a challenge for both experiment and theory due to its short lifetime and high reactivity, and the need for a high-level of electronic structure theory to achieve predictive accuracy. The lack of a classical atomistic structural formula makes it exceedingly difficult to model the solvated electron using conventional empirical force fields, which describe the system in terms of interactions between point particles associated with atomic nuclei. Here we overcome this problem using a machine-learning model, that is sufficiently flexible to describe the effect of the excess electron on the structure of the surrounding water, without including the electron in the model explicitly. The resulting potential is not only able to reproduce the stable cavity structure but also recovers the correct localization dynamics that follow the injection of an electron in neat water. The machine learning model achieves the accuracy of the state-of-the-art correlated wave function method it is trained on. It is sufficiently inexpensive to afford a full quantum statistical and dynamical description and allows us to achieve accurate determination of the structure, diffusion mechanisms, and vibrational spectroscopy of the solvated electron.
由于其寿命短、反应活性高，以及需要高水平的电子结构理论来实现预测精度，大量水合电子的性质对实验和理论来说都是一个挑战。缺乏经典的原子结构公式使得使用传统的经验力场来模拟溶剂化电子极其困难，传统经验力场是根据与原子核相关的点粒子之间的相互作用来描述系统的。在这里，我们使用机器学习模型克服了这个问题，该模型足够灵活，可以描述多余电子对周围水分子结构的影响，而无需在模型中明确包含电子。由此产生的势不仅能够重现稳定的空腔结构，还能恢复在纯水中注入电子后正确的局域化动力学。该机器学习模型达到了其训练所基于的最先进相关波函数方法的精度。它成本足够低，能够提供完整的量子统计和动力学描述，并使我们能够准确确定溶剂化电子的结构、扩散机制和振动光谱。
