With the establishment of machine learning (ML) techniques in the scientific community, the construction of ML potential energy surfaces (ML-PES) has become a standard process in physics and chemistry. So far, improvements in the construction of ML-PES models have been conducted independently, creating an initial hurdle for new users to overcome and complicating the reproducibility of results. Aiming to reduce the bar for the extensive use of ML-PES, we introduce ${\it Asparagus}$, a software package encompassing the different parts into one coherent implementation that allows an autonomous, user-guided construction of ML-PES models. ${\it Asparagus}$ combines capabilities of initial data sampling with interfaces to ${\it ab initio}$ calculation programs, ML model training, as well as model evaluation and its application within other codes such as ASE or CHARMM. The functionalities of the code are illustrated in different examples, including the dynamics of small molecules, the representation of reactive potentials in organometallic compounds, and atom diffusion on periodic surface structures. The modular framework of ${\it Asparagus}$ is designed to allow simple implementations of further ML-related methods and models to provide constant user-friendly access to state-of-the-art ML techniques.

翻译：随着机器学习（ML）技术在科学界的广泛应用，构建机器学习势能面（ML-PES）已成为物理学和化学领域的标准流程。目前，ML-PES模型的构建改进工作往往独立进行，这给新用户设置了初始门槛，也增加了结果复现的复杂性。为降低ML-PES广泛使用的壁垒，我们推出《Asparagus》软件包，该工具将不同构建环节整合为连贯的实现框架，支持自主化、用户引导式的ML-PES模型构建。Asparagus集成了初始数据采样、与第一性原理计算程序的接口、ML模型训练、模型评估以及在ASE或CHARMM等代码中的应用功能。本文通过多个示例展示该代码的功能，包括小分子动力学模拟、有机金属化合物反应势能表征以及周期性表面结构的原子扩散过程。Asparagus采用模块化框架设计，可便捷集成更多ML相关方法与模型，为用户提供持续友好的前沿ML技术访问途径。