# it Asparagus: A Toolkit for Autonomous, User-Guided Construction of Machine-Learned Potential Energy Surfaces

arXiv: https://arxiv.org/abs/2407.15175 · HTML: https://mlsanity.com/paper/2407.15175/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 92 references that are in the map.
- First posted on arXiv 21 Jul 2024.

## Builds on (most cited first, 14 of 14)

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- [Machine Learning Force Fields](https://mlsanity.com/paper/2010.07067/) (2020)
- [Less is more: sampling chemical space with active learning](https://mlsanity.com/paper/1801.09319/) (2018)
- [DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics](https://mlsanity.com/paper/1712.03641/) (2017)
- [TorchMD: A deep learning framework for molecular simulations](https://mlsanity.com/paper/2012.12106/) (2020)
- [SchNetPack 2.0: A neural network toolbox for atomistic machine learning](https://mlsanity.com/paper/2212.05517/) (2022)
- [MLatom 3: Platform for machine learning-enhanced computational chemistry simulations and workflows](https://mlsanity.com/paper/2310.20155/) (2023)
- [FeNNol: an Efficient and Flexible Library for Building Force-field-enhanced Neural Network Potentials](https://mlsanity.com/paper/2405.01491/) (2024)
- [Uncertainty quantification for predictions of atomistic neural networks](https://mlsanity.com/paper/2207.06916/) (2022)
- [Metadynamics for Training Neural Network Model Chemistries: a Competitive Assessment](https://mlsanity.com/paper/1712.07240/) (2017)

## Abstract (arXiv)

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.