用极简神经网络构建高精度分子势能面，训练快且外推能力强。
The Bigger the Better? Accurate Molecular Potential Energy Surfaces from Minimalist Neural Networks
- 结合核方法与神经网络，大幅减少可学习参数
- 训练和推理速度提升数个数量级，精度仍保持高水平
- 适合需要快速、准确势能面的分子动力学模拟场景
原子级模拟是研究分子、蛋白质和材料在宽时间与空间尺度上动态行为的强大工具。其可靠性与预测能力直接依赖于底层势能面（PES）的准确性。受简约性原则启发，本文提出KerNN——一种基于核函数与神经网络结合的分子PES表示方法。相比当前最先进的神经网络势能面，KerNN的可学习参数显著减少，使训练与评估速度提升数个数量级，同时保持高预测精度。重要的是，采用核函数作为特征显著增强了KerNN的外推能力，远超训练数据覆盖范围，解决了神经网络基势能面普遍存在的外推难题。KerNN在光谱与反应动力学任务中表现优异，测试集统计量及可观测量（包括经典与量子模拟计算的振动谱带）均达到良好效果。
原文摘要 · Abstract (English)
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.
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