Add CGNEP reference to README

Added reference for CGNEP, a coarse-grained machine learning potential for multilayered graphene.

Author: brucefan1983

README.md

@@ -77,9 +77,11 @@ There is a standalone C++ implementation of the neuroevolution potential (NEP) i
 | [Liang2025]                  | NEP89 (Universal neuroevolution potential for inorganic and organic materials across 89 elements) |
 | [Huang2026]                  | GNEP: An alternative training scheme for NEP models |
 | [Fan2026a]                  | qNEP: NEP with dynamic charge (q) |
+| [Li2025]                    | CGNEP: Coarse-grained machine learning potential for mesoscale multilayered graphene (https://github.com/lmqnuaa/CGNEP) |
 | [Fan2026b]                  | NEP-CG and NEP-AACG: NEP with coarse graining |
 | [Bu2026]                  | NEP + ILP (hybrid NEP with anisotropic interlayer potential) |
 
+
 ## References
 
 [Xu2025] Ke Xu, Hekai Bu, Shuning Pan, Eric Lindgren, Yongchao Wu, Yong Wang, Jiahui Liu, Keke Song, Bin Xu, Yifan Li, Tobias Hainer, Lucas Svensson, Julia Wiktor, Rui Zhao, Hongfu Huang, Cheng Qian, Shuo Zhang, Zezhu Zeng, Bohan Zhang, Benrui Tang, Yang Xiao, Zihan Yan, Jiuyang Shi, Zhixin Liang, Junjie Wang, Ting Liang, Shuo Cao, Yanzhou Wang, Penghua Ying, Nan Xu, Chengbing Chen, Yuwen Zhang, Zherui Chen, Xin Wu, Wenwu Jiang, Esme Berger, Yanlong Li, Shunda Chen, Alexander J. Gabourie, Haikuan Dong, Shiyun Xiong, Ning Wei, Yue Chen, Jianbin Xu, Feng Ding, Zhimei Sun, Tapio Ala-Nissila, Ari Harju, Jincheng Zheng, Pengfei Guan, Paul Erhart, Jian Sun, Wengen Ouyang, Yanjing Su, Zheyong Fan, [GPUMD 4.0: A high-performance molecular dynamics package for versatile materials simulations with machine-learned potentials]( https://doi.org/10.1002/mgea.70028), MGE Advances **3**, e70028 (2025).
@@ -148,6 +150,8 @@ Computer Physics Communications **320**, 109994 (2026).
 
 [Fan2026a] Zheyong Fan, Benrui Tang, Esmée Berger, Ethan Berger, Erik Fransson, Ke Xu, Zihan Yan, Zhoulin Liu, Zichen Song, Haikuan Dong, Shunda Chen, Lei Li, Ziliang Wang, Yizhou Zhu, Julia Wiktor, Paul Erhart [qNEP: A highly efficient neuroevolution potential with dynamic charges for large-scale atomistic simulations](https://arxiv.org/abs/2601.19034), arXiv:2601.19034 [physics.comp-ph].
 
+[Li2025] Mingqian Li, Lifeng Wang, Zhuoqun Zheng, [Coarse-grained machine learning potential for mesoscale multilayered graphene](https://doi.org/10.1038/s41524-025-01849-2), npj Computational Materials **11**, 374 (2025).
+
 [Fan2026b] Zheyong Fan, Wenjun Zhang, Zhenhao Zhang, Ke Xu, Xuecheng Shao, Haikuan Dong, [NEP-CG and NEP-AACG: Efficient coarse-grained and multiscale all-atom-coarse-grained neuroevolution potentials](https://arxiv.org/abs/2603.01234), arXiv:2603.01234 [physics.comp-ph] (2026).
 
 [Bu2026] Hekai Bu, Wenwu Jiang, Penghua Ying, Ting Liang, Zheyong Fan, and Wengen Ouyang, [Modular hybrid machine learning and physics-based potentials for scalable modeling of Van der Waals heterostructures](https://doi.org/10.1016/j.jmps.2026.106540), J. Mech. Phys. Solids **210**, 106540 (2026).