While machine learning (ML) interatomic potentials (IPs) are able to achieve accuracies nearing the level of noise inherent in the first-principles data to which they are trained, it remains to be shown if their increased complexities are strictly necessary for constructing high-quality IPs. In this work, we introduce a new MLIP framework which blends the simplicity of spline-based MEAM (s-MEAM) potentials with the flexibility of a neural network (NN) architecture. The proposed framework, which we call the spline-based neural network potential (s-NNP), is a simplified version of the traditional NNP that can be used to describe complex datasets in a computationally efficient manner. We demonstrate how this framework can be used to probe the boundary between classical and ML IPs, highlighting the benefits of key architectural changes. Furthermore, we show that using spline filters for encoding atomic environments results in a readily interpreted embedding layer which can be coupled with modifications to the NN to incorporate expected physical behaviors and improve overall interpretability. Finally, we test the flexibility of the spline filters, observing that they can be shared across multiple chemical systems in order to provide a convenient reference point from which to begin performing cross-system analyses.

在这项工作中，我们引入了一种新的机器学习内原子势函数（MLIP）框架，将基于样条的MEAM势（s-MEAM）与神经网络（NN）架构的灵活性相结合。这种框架被称为基于样条的神经网络势（s-NNP），是传统NNP的简化版本，可以以计算高效的方式描述复杂的数据集。我们演示了如何使用这个框架来探索经典和ML内原子势函数之间的边界，并突出了关键结构变化的好处。此外，我们展示了使用样条滤波器对原子环境进行编码会产生容易解释的嵌入层，可以与NN的修改相结合，以纳入预期的物理行为并改善整体可解释性。最后，我们测试了样条滤波器的灵活性，观察到它们可以在多个化学系统之间共享，以提供一个方便的参考点，从而开始进行跨系统分析。

基于样条的神经网络原子势函数: 融合经典和机器学习模型