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.
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