姓　名：汪海迪

职　称：副研究员

职　务：副系主任

所属系：物理系

邮　箱：haidi@hfut.edu.cn

个人简介：

汪海迪，男，副研究员，硕导。2012年毕业于南京师范大学应用化学专业，获理学学士学位；2018年毕业于中国科学技术大学微尺度物质科学国家实验中心物理化学专业，获理学博士学位 。2018-2019年在Iowa State University做博士后研究工作。2019.06-2019.12在北京大学大数据研究院参与深度势能研发工作 ；2019年至今就职于合肥工业大学，开展生成式人工智能驱动的材料设计、磁性材料高通量计算、机器学习势函数以及材料数据库开发等研究。至今已在Advanced Functional Materials, Research、Materials Horizons、Applied Surface Science、Nanoscale、2D Materials等期刊上发表论文30余篇，并应邀担任Nanoscale、JPCC等期刊的审稿人。

研究方向：

低维材料

高通量计算

深度学习势

材料数据库开发

自然语言处理

开设课程（本科生、研究生）：

《大学物理》、《量子力学》

科研项目：

1. 合肥工业大学人才引进科研启动经费，2019/04-2024/04，20万，主持

2. 合肥工业大学学术新人提升计划A项目，双极磁性半导体材料高通量筛选及数据库设计，2022/01-2023/01, 5万，主持

2. 合肥工业大学学术新人提升计划B项目，二维材料通用预训练深度学习势能面模型研究，2024/01-2025/01, 20万，主持

4. 国家自然科学青年基金项目, 基于金属有机框架的二维负泊松比材料理论设计与调控研究，2023/01-2025/01, 30万，主持

代表成果：

1. Xiaofeng Liu, Yihang Bai, Weiduo Zhu, Zhao Liu, Zhao Chen, Pengfei Gao, Haidi Wang*, Zhongjun Li*, Bing Wang, Xingxing Li, Wei Hu*, Jinlong Yang. Molecule‐Induced Huge d‐p Overlap Enhances Superexchange Interaction for Room‐Temperature In‐Plane Magnetism and Giant Magneto Band‐Structure Effect in Ferromagnetic Clusterphene. Adv. Func. Mater.,2024, 2414984

2. Wei Lin, Huimiao Wang, Yaling Luo, Xiaofeng Liu, Zhongjun Li, Weiduo Zhu, Xiaofeng Liu, Zhao. Chen*, and Haidi Wang*. Two-Dimensional Multifunctional Metal–Organic Frameworks with Large In-Plane Negative Poisson Ratios and Photocatalytic Water Splitting Properties. Mater. Horiz. 2024, DOI https://doi.org/10.1039/D4MH01275E

3. Haidi Wang, Tao Li, Zhao Chen, Weiduo Zhu, Wei Lin, Huimiao Wang, Xiaofeng Liu*, and Zhongjun Li*. High Out-of-Plane Negative Poisson's Ratios and Strong Light Harvesting in Two-Dimensional SiS₂ and Its Derivatives. Nanoscale 2023, 15, 16155-16162 .

4. Haidi Wang, Tao Li, Xiaofeng. Liu, Weidu Zhu, Zhao Chen, Zhongjun Li* and Jinlong Yang*. mech2d: An Efficient Tool for High-Throughput Calculation of Mechanical Properties for Two-Dimensional Materials. Molecules 2023, 28(11), 4337.

5. Haidi Wang, Qingqing Feng, Xingxing Li, and Jinlong Yang. High-Throughput Computational Screening for Bipolar Magnetic Semiconductors. Research 2022, 9857631.

6. Haidi Wang*, Zhao Chen, and Zhao Liu*. Penta-CN₂ Revisited: Superior Stability, Synthesis Condition Exploration, Negative Poisson’s Ratio and Quasi-Flat Bands. Appl. Surf. Sci. 2022 585, 152536.

7. Xiaoning Wang, Haidi Wang*, Qiquan Luo, and Jinlong Yang*. Structural and Electrocatalytic Properties of Copper Clusters: A Study via Deep Learning and First Principles. J. Chem. Phys. 2022, 157, 074304.

8. Zhao Chen , ZhongJun Li, and Haidi Wang*, Two-Dimensional Auxetic GeSe₂ Material with Ferroelasticity and Flexoelectricity,  J. Phys. Chem. C 2021, 125, 36, 19666–19672

9. Haidi Wang, Yuzhi Zhang , Linfeng Zhang* and Han Wang*, Crystal structure prediction of binary alloys via deep potential. Front. Chem. 2020, 8:589795.doi: 10.3389/fchem.2020.589795

10. Haidi Wang, Xingxing Li, Pai Li and Jinlong Yang. δ-Phosphorene: a two dimensional material with a highly negative poisson's ratio, Nanoscale, 2017,9, 850-855;

11. Haidi Wang, Xingxing Li, Jiuyu Sun, Zhao Liu and Jinlong Yang*. BP₅ monolayer with multiferroicity and negative Poisson's ratio: a prediction by global optimization method, 2D Materials 2017, 4, 045020;

12. Zhao Liu#, Haidi Wang#, Jiuyu Sun, Ruijie Sun, Zhengfei Wang* and Jinlong Yang* . Penta-Pt₂N₄: an ideal two-dimensional material for nanoelectronics, Nanoscale 2018，10 (34), 16169-16177;

13. Haidi Wang, Bin Li, Jinlong Yang*. Electronic, optical, and mechanical properties of diamond nanowires encapsulated in carbon nanotubes: a first-principles View, J. Phys. Chem. C. 2017,121, 3661–3672;

14. Haidi Wang, Xingxing Li, Zhao Liu and Jinlong Yang. ψ-Phosphorene: a New allotrope of phosphorene, Phys. Chem. Chem. Phys., 2017,19, 2402-2408;

15. Zhao Liu, Haidi Wang, ZhengFei Wang*, Jinlong Yang*, Feng Liu, Pressure-induced organic topological nodal-line semimetal in the three-dimensional molecular crystal , Physical Review B, 97 (15), 155138;

16. Yuzhi Zhang, Haidi Wang, Weijie Chen, Jinzhe Zeng, Linfeng Zhang, Han Wang*, Weinan E*. DP-GEN: A concurrent learning platform for the generation of reliable deep learning based potential energy models,Computer Physics Communications, 2020,107206;

软著：

mech2d软件和mvd材料数据库软件著作权

学生培养：

课题组林伟同学获得2024年度研究生国家奖学金

硕士招生（每年1-2名）：

具备良好的量子力学（化学）与固体物理基础；熟练掌握编程，尤其是 Python 等语言；具有 Web 开发、数据库设计以及自然语言处理经验者优先。

个人主页：link