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.

翻译：尽管机器学习原子间势能（IPs）能够达到与其训练的第一性原理数据固有噪声水平相当的精度，但尚需证明其增加的复杂性对于构建高质量IPs是否绝对必要。本文提出一种新型机器学习原子间势框架，将基于样条函数的修正嵌入原子法（s-MEAM）势的简洁性与神经网络（NN）架构的灵活性相结合。该框架被称为基于样条函数的神经网络势（s-NNP），是传统NNP的简化版本，能够以高效计算的方式描述复杂数据集。我们展示了该框架如何用于探索经典IP与机器学习IP之间的界限，凸显关键架构改进的优势。此外，研究表明，采用样条滤波器编码原子环境可形成易于解释的嵌入层，该层能通过神经网络修改融入预期物理行为，从而提升整体可解释性。最后，我们测试了样条滤波器的灵活性，发现其可在多个化学体系中共享，为开展跨体系分析提供便捷的参考基准。