论文标题
机器学习潜力的可转移性：质子化水神经网络电位应用于质子化水六聚体
Transferability of machine learning potentials: Protonated water neural network potential applied to the protonated water hexamer
论文作者
论文摘要
先前发表的神经网络潜力，用于描述质子化水簇至质子化水四聚体，H $^+$（H $ _2 $ o）$ _ 4 $，在本质上融合的耦合群集准确性（J. Chem Theory Comput。16，88（2020））适用于Protonated Water hexamer，h $ ____________________________________________________________-神经网络从未见过。尽管处于推断状态，但表明潜力不仅允许从超低温度$ \ sim $ 1 K $ 1 K至300 K进行量子模拟，而且能够非常准确地描述新系统与显式耦合群集计算。与模型的训练集中的环境相比，该模型的这种可传递性是通过在较大集群中遇到的原子环境的相似性合理化的。与插值制度相比，模型的质量大约是一个数量级，但是耦合群集参考的大多数差异来自势能表面的全局移位，而局部能量波动则恢复了很好的回收率。这些结果表明，神经网络潜力在外推方案中的应用可以提供有用的结果，并且可能比平常想象的更一般。
A previously published neural network potential for the description of protonated water clusters up to the protonated water tetramer, H$^+$(H$_2$O)$_4$, at essentially converged coupled cluster accuracy (J. Chem. Theory Comput. 16, 88 (2020)) is applied to the protonated water hexamer, H$^+$(H$_2$O)$_6$ -- a system that the neural network has never seen before. Although being in the extrapolation regime, it is shown that the potential not only allows for quantum simulations from ultra-low temperatures $\sim$ 1 K up to 300 K, but that it is able to describe the new system very accurately compared to explicit coupled cluster calculations. This transferability of the model is rationalized by the similarity of the atomic environments encountered for the larger cluster compared to the environments in the training set of the model. Compared to the interpolation regime the quality of the model is reduced by roughly one order of magnitude, but most of the difference to the coupled cluster reference comes from global shifts of the potential energy surface, while local energy fluctuations are well recovered. These results suggest that the application of neural network potentials in extrapolation regimes can provide useful results and might be more general than usually thought.
