课后作业1 （提交截止日期：2025.10.21）
课后作业2 （提交截止日期：2025.11.04）
课后作业3 （提交截止日期：2025.12.16）
1. International Energy Agency, Energy and AI, 2025-04, https://www.iea.org/reports/energy-and-ai.
2. Aurelien Geron, Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow
3. 矩阵微积分参考读物https://web.mit.edu/course/14/14.380/www/handouts/matrixcalculus.pdf
课程展示文献列表：
电网优化：
1. Stephen J. Harris* and Marcus M. Noack, Statistical and machine learning-based durability-testing strategies for energy storage, Joule, 2023, 7, 920–934.
2. Subir Majumder, et al. Exploring the capabilities and limitations of large language models in the electric energy sector, Joule, 2023, 7, 1539–1555.
3. Hendrik F. Hamann, et al. Foundation models for the electric power grid, Joule, 2024, 8, 3245–3253.
4. Dylan Harrison-Atlas, Andrew Glaws, Ryan N. King, and Eric Lantz, Artificial intelligence-aided wind plant optimization for nationwide evaluation of land use and economic benefits of wake steering, Nature Energy, 2024, 9, 735–749.
5. Grey Nearing*, et al. Foundation models for the electric power grid, Nature, 2024, 627, 559–563.
电池：
6. Kristen A. Severson, et al. Data-driven prediction of battery cycle life before capacity degradation, Nature Energy, 2019, 4, 383–391.
7. Peter M. Attia, et al. Closed-loop optimization of fast-charging protocols for batteries with machine learning, Nature, 2020,578, 397–402.
8. Adarsh Dave, JaredMitchell, Sven Burke, Hongyi Lin, Jay Whitacre*, and Venkatasubramanian Viswanathan*, Autonomous optimization of non-aqueous Li-ion battery electrolytes via robotic experimentation and machine learning coupling, Nature Communications, 2022, 13, 5454.
9. Shengyu Tao, et al. Collaborative and privacy-preserving retired battery sorting for profitable direct recycling via federated machine learning, Nature Communications, 2023, 14, 8032.
10. Ioan-Bogdan Magdău*, et al. Machine learning force fields for molecular liquids: Ethylene Carbonate/Ethyl Methyl Carbonate binary solvent, npj Computational Materials, 2023, 9, 146.
11. Nathan J. Szymanski, et al. An autonomous laboratory for the accelerated synthesis of novel materials, Nature, 2023, 624, 86-91.
11. Fuzhan Rahmanian*, et al. Attention towards chemistry agnostic and explainable battery lifetime prediction, npj Computational Materials, 2024, 10, 100.
12. Shang Zhu, et al. Differentiable modeling and optimization of non-aqueous Li-based battery electrolyte solutions using geometric deep learning, Nature Communications, 2024, 15, 8649.
13. Chi Chen, et al. Accelerating Computational Materials Discovery with Machine Learning and Cloud High-Performance Computing: from Large-Scale Screening to Experimental Validation, J. Am. Chem. Soc. 2024, 146, 20009−20018.
14. Rui Cao, Zhengjie Zhang, Runwu Shi , Jiayi Lu, Yifan Zheng , Yefan Sun, Xinhua Liu, and Shichun Yang*, Model-constrained deep learning for online fault diagnosis in Li-ion batteries over stochastic conditions, Nature Communications, 2025, 16, 1653.
15. Xiang Chen,* Mingkang Liu, Shiqiu Yin, Yu-Chen Gao, Nan Yao, and Qiang Zhang*, Uni-Electrolyte: An artificial intelligence platform for designing electrolyte molecules for rechargeable batteries, Angew. Chem. Int. Ed. 2025, 64, e202503105.
催化剂:
16. Nung Siong Lai, Yi Shen Tew, Xialin Zhong, Jun Yin, Jiali Li, Binhang Yan, and Xiaonan Wang*, Artificial intelligence (AI) workflow for catalyst design and optimization, Ind. Eng. Chem. Res. 2023, 62, 17835−17848.
17. Zhilong Song, Linfeng Fan, Shuaihua Lu, Chongyi Ling, Qionghua Zhou*, and Jinlan Wang*, Inverse design of promising electrocatalysts for CO2 reduction via generative models and bird swarm algorithm, Nature Communications, 2025, 16, 1053.
18. Zhen Zhang, et al. A multimodal robotic platform for multi-element electrocatalyst discovery, Nature, 2025, 647, 390-396.
19. Negin Orouji, et al. Autonomous catalysis research with human–AI–robot collaboration, Nature Catalysis, 2025, 8, 1135–1145.
CO2捕获:
20. Anuroop Sriram*, et al. The open DAC 2023 dataset and challenges for sorbent discovery in direct air capture, ACS Cent. Sci. 2024, 10, 923−941.
21. Hyun Park, et al. A generative artificial intelligence framework based on a molecular diffusion model for the design of metal-organic frameworks for carbon capture, Communications Chemistry, 2024, 7, 21.
教学目标：
本课程面向能源动力、化学工程和化学等专业的研究生，旨在介绍人工智能 (AI) 的基本概念、技术和应用，并深入探讨 AI 在能源领域的应用前景和挑战，特别是结合电池储能在绿色低碳的可再生能源技术中应用背景，重点讲解 AI 在电池储能系统中的应用。课程内容涵盖 Python 编程基础、人工智能基础、机器学习、深度学习、以及 AI 在能源优化、能源创新和新兴领域中的应用。
课程注重理论与实践相结合，培养学生利用 AI 技术解决能源领域实际问题的能力。通过学习本课程，学生将掌握 Python 编程语言基础，了解人工智能的基本概念、发展历史、主要技术和方法，熟悉 AI 在能源领域的应用现状和发展趋势，并具备利用 AI 技术解决能源领域实际问题的初步能力，为未来从事能源与人工智能交叉领域的研究和工作打下坚实基础。
教学安排：
参考教材：
1. 蔡自兴，刘丽珏，陈白帆，蔡昱峰，人工智能及其应用 ，清华大学出版社 ，2024-02-01 第七版。
2. Jeffery Heaton著，李尔超 和王海鹏译 ，人工智能算法 （卷1、2、3），人民邮电出版社 ，2020-01-01、2020-11-01、2021-04-01。
3. Aurelien Geron 著 ，Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow ，东南大学出版社 ，2023-03-01 。
4. 陆文聪 等著 ，基于机器学习的材料设计 ，科学出版社 ，2024-08-01 。
5. 杨博 著 ，人工智能在新能源发电系统中的应用 ，中国电力出版社 ，2023-10-01 。
6. International Energy Agency, Energy and AI, 2025-04, https://www.iea.org/reports/energy-and-ai.
考核方式：
1. 课堂表现(10/100): 出勤率及课堂参与度。
2. 课后作业 (20/100): 将会布置5次课后作业，每次40分，5次作业总成绩将计入最终成绩。
3. 随堂测验 (20/100):
测验1 (9.28 or 9.30)
测验2 (10.28 or 11.04)
测验3 (11.25 or 12.02)
两次最高分将用于计算最终成绩。
4. 文献汇报 (10/100):
选择一篇将AI应用于能源研究的论文进行课堂展示（12分钟展示 + 3分钟提问）。
5. 课程项目 (40/100):
选择一个课程项目，构建和训练机器学习模型，撰写项目报告，并于1月25日之前邮件提交至ke.yang@nankai.edu.cn。