Asparagus: A toolkit for autonomous, user-guided construction of machine-learned potential energy surfaces

• Published in: Computer physics communications 2025-03, Vol.308, p.109446, Article 109446
• Main Authors: Töpfer, Kai, Vazquez-Salazar, Luis Itza, Meuwly, Markus
• Format: Article
• Language: English
• Subjects: Machine learning; Neural networks; Potential energy surfaces
• ISSN: 0010-4655
• DOI: 10.1016/j.cpc.2024.109446

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 Asparagus, a software package encompassing the different parts into one coherent implementation that allows an autonomous, user-guided construction of ML-PES models. Asparagus combines capabilities of initial data sampling with interfaces to 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 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. Program Title:Asparagus CPC Library link to program files:https://doi.org/10.17632/9w9xw7mp2h.1 Developer's repository link:https://github.com/MMunibas/Asparagus Licensing provisions: MIT Programming language: Python Supplementary material: Access to Documentation at https://asparagus-bundle.readthedocs.io Nature of problem: Constructing machine-learning (ML) based potential energy surfaces (PESs) for atomistic simulations is a multi-step process that requires a broad knowledge in quantum chemistry, nuclear dynamics and programming. So far, efforts mainly focused on developing and improving ML model architectures. However, there was less effort spent on providing tools for consistent and reproducible workflows that support the construction of ML-PES for a variety of chemical systems for the broader science community. Solution method:Asparagus is a program package written in Python that provides a streamlined and extensible workflow with a user-friendly command structure to support the construction of ML-PESs. This is achieved by bundling and linking data generation and sampling techniques, data management, model training, testing and evaluation tools into one modular, comprehensive workflow includi