NEP89: universal neuroevolution potential for inorganic and organic materials across 89 elements
Journal article, 2026
While machine-learned interatomic potentials offer near-quantum-mechanical accuracy for atomistic simulations, many are material-specific or computationally intensive, limiting their broader use. Here we introduce NEP89, a foundation model based on neuroevolution potential architecture, delivering near-empirical-potential speed and high accuracy across 89 elements. A compact yet comprehensive training dataset covering inorganic and organic materials was curated through descriptor-space subsampling and iterative refinement across multiple datasets. NEP89 achieves competitive accuracy compared with representative foundation models while being three to four orders of magnitude more computationally efficient, enabling previously impractical large-scale atomistic simulations of inorganic and organic systems. In addition to its out-of-the-box applicability to diverse scenarios, including million-atom-scale compression of compositionally complex alloys, ion diffusion in solid-state electrolytes and water, rocksalt dissolution, methane combustion and protein-ligand dynamics, NEP89 also supports fine-tuning for rapid adaptation to user-specific applications, such as mechanical, thermal, structural and spectral properties of two-dimensional materials, metallic glasses and organic crystals.
Author
Ting Liang
Chinese University of Hong Kong
Bohai University
Ke Xu
Chinese University of Hong Kong
Bohai University
Eric Lindgren
Chalmers, Physics, Condensed Matter and Materials Theory
Zherui Chen
Shenzhen University
Shenzhen Technology University
Rui Zhao
Xinyu University
Jiahui Liu
Beijing University of Technology
Esmée Berger
Chalmers, Physics, Condensed Matter and Materials Theory
Benrui Tang
Bohai University
Bohan Zhang
Bohai University
Yanzhou Wang
Aalto University
Keke Song
Fuzhou University
Penghua Ying
Tel Aviv University
Nan Xu
Zhejiang University
Haikuan Dong
Bohai University
Shunda Chen
George Washington University
Paul Erhart
Chalmers, Physics, Condensed Matter and Materials Theory
Zheyong Fan
Bohai University
Tapio Ala-Nissila
Loughborough University
Aalto University
Jianbin Xu
Chinese University of Hong Kong
NATURE COMPUTATIONAL SCIENCE
2662-8457 (eISSN)
Vol. In PressHydrogen trapping by carbides in steel
Swedish Research Council (VR) (2021-05072), 2021-12-01 -- 2025-11-30.
Phase behavior and electronic properties of mixed halide perovskites from atomic scale simulations
Swedish Research Council (VR) (2020-04935), 2020-12-01 -- 2024-11-30.
SwedNESS
Swedish Foundation for Strategic Research (SSF) (GSn15-0008), 2016-07-01 -- 2021-06-30.
Swedish Foundation for Strategic Research (SSF) (GSn15-0008), 2017-01-01 -- 2020-12-31.
Subject Categories (SSIF 2025)
Materials Chemistry
DOI
10.1038/s43588-026-01009-6
PubMed
42420553