Authors: Ting Liang | Ke Xu | Eric Lindgren | Zherui Chen | Rui Zhao | Jiahui Liu | Esmée Berger | Benrui Tang | Bohan Zhang | Yanzhou Wang | Keke Song | Penghua Ying | Nan Xu | Haikuan Dong | Shunda Chen | Paul Erhart | Zheyong Fan | Tapio Ala-Nissila | Jiayi Xu
Source: Open MIND (article, 2026-04-07)
Field: Materials Science — Materials Chemistry

Dataset and models supporting "NEP89: Universal neuroevolution potential for inorganic and organic materials across 89 elements" Description This repository contains the dataset, source code, demonstration files, and fine-tuning workflows for NEP89, a universal Neuroevolution Potential (NEP) foundation model covering 89 elements across the periodic table, as described in Liang et al., "NEP89: Universal neuroevolution potential for inorganic and organic materials across 89 elements" [arXiv:2504.21286]. Included Files Dataset_for_NEP89.zip This archive contains the comprehensive dataset used to train the NEP89 model. It comprises carefully subsampled structures from diverse sources (e.g., OMAT24, MPtrj, SPICE, ANI-1xnr, UNEP-v1, solid-state electrolytes, water, protein, and CH systems), along with newly constructed CHONPS data. The dataset is organized into 11 distinct subsets, which can be used independently or combined into a unified dataset for machine-learned potential (MLP) training and development. GPUMD-5.0-NEP89-Demos.zip This archive provides the essential software environment and practical examples for the NEP89 model, including: Source code for NEP89 model deployment and performing Molecular Dynamics (MD) simulations. Out-of-the-box demos for rapid testing of the NEP89 model. Fine-tuning workflows for adapting the NEP89 model to specific chemical environments or high-accuracy requirements. Source_data_for_NEP89.zip This archive contains the source data and plotting scripts for selected figures in the main text of the NEP89 paper. It is intended to support the transparency, reproduction, inspection, and validation of the published results. The archive includes the raw data files underlying the figures, together with the corresponding scripts used to generate them. Additional details on file organization and usage are provided in the README files within the archive. Quick Links & Resources For the most up-to-date resources and detailed instructions, please refer to: NEP89 Model: The latest NEP89 potential files are available in the GPUMD GitHub Repository. Fine-tuning Tutorial: A step-by-step guide to fine-tuning NEP89 is available in the GPUMD-Tutorials. Software Requirement: For optimal performance and compatibility, especially during fine-tuning, we recommend using the latest version of GPUMD (version 5.0). Additional Information Further technical details and implementation notes are provided in the README files within each archive. For repository- and implementation-related inquiries, please contact: liangting.zj@gmail.com, phychensd@gmail.com, brucenju@gmail.com Workflow ; Neuroevolution ; Software ; Source code ; Code (set theory) ; Artificial neural network ; Open source Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Model Reduction and Neural Networks