Atomistic simulations are a powerful tool for studying the dynamics of molecules, proteins, and materials on wide time and length scales. Their reliability and predictiveness, however, depend directly on the accuracy of the underlying potential energy surface (PES). Guided by the principle of parsimony this work introduces KerNN, a combined kernel/neural network-based approach to represent molecular PESs. Compared to state-of-the-art neural network PESs the number of learnable parameters of KerNN is significantly reduced. This speeds up training and evaluation times by several orders of magnitude while retaining high prediction accuracy. Importantly, using kernels as the features also improves the extrapolation capabilities of KerNN far beyond the coverage provided by the training data which solves a general problem of NN-based PESs. KerNN applied to spectroscopy and reaction dynamics shows excellent performance on test set statistics and observables including vibrational bands computed from classical and quantum simulations.

本研究解决了分子势能面（PES）准确性不足的问题，提出了一种名为KerNN的核/神经网络结合方法，该方法显著减少了可学习参数的数量，从而加速了训练和评估过程，同时保持了高预测准确性。KerNN的改进外推能力超过了传统神经网络PES，展现了在光谱学和反应动力学中的优异表现。

越大越好吗？来自简约神经网络的准确分子势能面