Hot Keywords



  • Prof. Linfeng Zhang

    DP Technology, Beijing, China;
    AI for Science Institute, Beijing, China.

    Linfeng Zhang is the founder and chief scientific officer of DP Technology and a researcher at the AI for Science Institute. In 2020, he graduated from the Program in Applied and Computational Mathematics at Princeton University. Linfeng has been focusing on developing machine-learning based physical models for electronic structures, molecular dynamics, and enhanced sampling. He's one of the main developers of DeePMD-kit, a popular deep learning-based open-source software for molecular simulation in physics, chemistry, and materials science. He is a recipient of the 2020 ACM Gordon Bell Prize.


  • Prof. Weinan E

    AI for Science Institute, Beijing, China;
    Center for Machine Learning Research, Peking University, Beijing, China.

    Weinan E is a professor at the Center for Machine Learning Research (CMLR) and the School of Mathematical Sciences at Peking University. His main research interest is numerical algorithms, machine learning and multi-scale modeling, with applications to chemistry, material sciences and fluid mechanics. He was a plenary speaker at ICM 2022 and a keynote speaker at ICML 2022. He has also been an invited speaker at APS, ACS, AIChe annual meetings, the World Congress of Computational Mechanics, and the American Conference of Theoretical Chemistry. Weinan E was awarded the ICIAM Collatz Prize in 2003 and the ACM Gordon-Bell Prize in 2020. He is a member of the Chinese Academy of Sciences, a fellow of SIAM, AMS and IOP.


In the last few years, a lot of progress has been made on machine learning-based multi-scale modeling. One area that has particularly benefitted from this is the study of materials. However, many serious obstacles still remain in order to fully realize the potential of this new approach. In this talk, I will give a review of the progress made, and discuss the main challenges that we face in this promising new direction.


  • Prof. Weinan E
    Topic: AI-Based Multi-scale Modeling for Materials
  • Topic: Free discussion
  • Topic: JMI introduction
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