Renewable Energy + AI + Complex Network (Superintelligence)
Research Interests -- Ab-initio Science and Enginnering 

Research Interests: Renewable energy and AI will define the future of humanity.  My research spectrum covers the discovery of novel energy material, the design of the smart battery management system, the study of complex power grids, and the development of novel algorithms for complex quantum dynamics, and beyond. We focus on applying the cutting-edge physics-based computational models, novel algorithms, and AI techniques in our research. Details can be found in Research Highlights.

Ab-initio Engineering: We are devoted to design and optimize distributed generations according to the first principle (Ab-initio). We aim to develop a novel distributed management system based on AI techniques and novel predictive analytics to manage energy storage, renewable energy, distributed generators, and etc.. Pls visit

We also have feature engineering projects, "Engineer Feature, Predict Future“

email: xin.chen.nj at || xin.chen.nj at

  • 我们有关人工智能动力电池预测分析的ppt. 其中的正在开展的工作包括 IoT和Edge Computing 技术在电力系统,尤其是可再生能源的管理和利用方面应用。
  • 我在”演绎inSite, DeepTech深科技“做的关于储能,电力系统,复杂网络,分布式调控,生物体系的视频文档
  • 我们收集并整理了电池状态预测分析 (Predictive Analytics), 人工智能(AI-Powered)算法和管理系统(BMS)相关文献(ongoing project)。
  • 我们最新的两篇关于陶瓷光伏材料的材料基因计算筛选文章,

1. "Layered Hexagonal Oxycarbides, M n+1 AO 2 X n (M=Sc, Y, La, Cr and Mo, A=Ca, X=C): Unexpected Photovoltaic Ceramics", The Journal of Physical Chemistry C 122(26), 2018 DOI: 10.1021/acs.jpcc.8b00905

2. "Classification of MAOX Phases and Semiconductor Screening for Next-Generation Energy Conversion Ceramic Materials", Journal of Materials Chemistry C, DOI: 10.1039/C9TC01078E  (封面文章, back)

  • 我们研究小组刚获得加州伯克利Rigetti量子云5000USD的Credit.
  • 我们团队做的在线预测预测系统的Demo(现在还是Alpha Version,更多内容后续更新), 

我的研究小组一直欢迎有兴趣从事前沿交叉研究的本科二三年级的学生加入 (背景不限)。研究项目包括大数据特征工程,纳米近场衰减波,超材料,纳米光子材料研究,陶瓷光伏材料基因研究,人工智能时间序列研究,人工智能动力电池预测分析和故障异常行为判断,和以及(电力)复杂网络。相关招聘信息可以通过招聘网页了解研究小组硕士生马亚伟和隋宇轩已经毕业,先在华为大数据和阿里巴巴工作。


Our team deveolped the framework of AI-Powered Energy Storage Ecosystem,  the details are discussed in

我们在发展电池状态虚实对应,数据驱动的人工智能电池管理生态系统。我们希望发展Date-Driven AI-Powered SOC/SOC Predictive Analystics 和 电池管理系统 (BMS),以及动力电池历史数据在动力电池梯度使用, 电池定价,电池资产管理等方面的应用。相关信息可以参考我们的PPT,

Want to know more about our work, click here!  

Classification of MAOX phases and semiconductor screening for next-generation energy conversion ceramic materials









Layered Hexagonal Oxycarbides, Mn+1AO2Xn : Unexpected Photovoltaic Ceramics




Lithium–Air Batteries: TiC MXene High Energy Density Cathode for Lithium–Air Battery 


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