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. 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: [email protected], [email protected], [email protected]
Authors
- Esmée Berger (ORCID: https://orcid.org/0000-0002-5282-7726)
- Keke Song
- Zherui Chen (ORCID: https://orcid.org/0000-0003-1318-1907)
- Paul Erhart (ORCID: https://orcid.org/0000-0002-2516-6061)
- Bohan Zhang (ORCID: https://orcid.org/0009-0009-8704-4041)
- Penghua Ying (ORCID: https://orcid.org/0000-0002-5758-2369)
- Shunda Chen (ORCID: https://orcid.org/0000-0002-5506-7507)
- Jiahui Liu (ORCID: https://orcid.org/0009-0001-3396-3482)
- Rui Zhao (ORCID: https://orcid.org/0000-0002-0312-8478)
- Nan Xu (ORCID: https://orcid.org/0000-0001-6739-3468)
- Benrui Tang
- Haikuan Dong
- Eric Lindgren
- Zheyong Fan
- Tapio Ala-Nissila
- Ke Xu
- Ting Liang
- Yanzhou Wang
Institutions
- Tel Aviv University (IL)
- Chinese University of Hong Kong (HK)
- George Washington University (US)
- Xinyu University (CN)
- Shenzhen Technology University (CN)
- Chalmers University of Technology (SE)
- Zhejiang University (CN)
- Fuzhou University (CN)
- University of Science and Technology Beijing (CN)
- Bohai University (CN)
- Aalto University (FI)
Publication Details
- Journal
- Zenodo (CERN European Organization for Nuclear Research)
- Published
- 2026-04-07
- DOI
- https://doi.org/10.5281/zenodo.19440424
- Primary Topic
- Machine Learning in Materials Science
- Type
- article
- Field-Weighted Citation Impact
- 0.00