NepTrain and NepTrainKit: Automated active learning and visualization toolkit for neuroevolution potentials

As a machine-learned potential, the neuroevolution potential (NEP) method features exceptional computational efficiency and has been successfully applied in materials science. Constructing high-quality training datasets is crucial for developing accurate NEP models. However, the preparation and screening of NEP training datasets remain a bottleneck for broader applications due to their time-consuming, labor-intensive, and resource-intensive nature. In this work, we have developed NepTrain and NepTrainKit, which are dedicated to initializing and managing training datasets to generate high-quality training sets while automating NEP model training. NepTrain is an open-source Python package that features a bond length filtering method to effectively identify and remove non-physical structures from molecular dynamics trajectories, thereby ensuring high-quality training datasets. NepTrainKit is a graphical user interface (GUI) software designed specifically for NEP training datasets, providing functionalities for data editing, visualization, and interactive exploration. It integrates key features such as outlier identification, farthest-point sampling, non-physical structure detection, and configuration type selection. The combination of these tools enables users to process datasets more efficiently and conveniently. Using CsPbI₃ as a case study, we demonstrate the complete workflow for training NEP models with NepTrain and further validate the models through materials property predictions. We believe this toolkit will greatly benefit researchers working with machine learning interatomic potentials. Program summary: Program Title: NepTrain and NepTrainKit CPC Library link to program files: https://doi.org/10.17632/4s97yg7j9t.1 Developer's repository link: https://github.com/aboys-cb/NepTrain and https://github.com/aboys-cb/NepTrainKit Licensing provisions: GPLv3 Programming language: Python Nature of problem: The NEP method, a novel machine learning potential model, has demonstrated broad application prospects in materials science due to its excellent computational efficiency. However, the development of accurate NEP models heavily depends on the construction of high-quality training datasets. The preparation and iterative refinement of these datasets largely rely on the researcher's expertise, which poses a significant barrier for beginners attempting to use NEP and similar machine learning potential methods. Solution method: NepTrain is an open-source Python package that features a bond length filtering method to effectively identify and remove non-physical structures from molecular dynamics trajectories, thereby ensuring high-quality training datasets. NepTrainKit is a graphical user interface (GUI) software designed specifically for NEP training datasets, providing functionalities for data editing, visualization, and interactive exploration. It integrates key features such as outlier identification, farthest-point sampling, non-physical structure detection, and configuration type selection.