上一篇 解曙方
鞠富松 下一篇
金沛然
北京中关村学院导师
乔治城大学物理学博士。已发表期刊与会议论文10篇，获国家发明专利授权7项。微软工作期间从事AI for Science方向的研究工作，聚焦蛋白质结构预测、小分子设计与蛋白质设计等关键任务，致力于构建统一的科学基础模型，推动人工智能在生命科学与材料科学中的落地转化。亦曾就职于Seagate科技，作为热辅助磁记录（HAMR）技术的核心研发成员，推动前沿物理技术的产业化落地，积累了丰富的跨学科工程经验。
I. 研究领域
AI for Science, 计算生物学，计算物理学
II. 教育经历
2013-2019， 乔治城大学物理系，博士
2009-2013， 中国科学技术大学物理学院，学士
III. 工作经历
2019-2021，希捷科技Research group，高级研发工程师
2022-2025，微软研究院 AI for Science，高级研发工程师
IV. 代表性学术论文
[1] Yingce Xia*, Peiran Jin*, Shufang Xie*, Liang He*, Chuan Cao*, Renqian Luo*, Guoqing Liu*, Yue Wang*, Zequn Liu*, Yuan-Jyue Chen*, Zekun Guo*, et al. NatureLM: Deciphering the Language of Nature for Scientific Discovery. arXiv, 2025.
[2] Xuerui Su, Shufang Xie, Guoqing Liu, Yingce Xia, Renqian Luo, Peiran Jin, Zhiming Ma, Yue Wang, Zun Wang, and Yuting Liu. Trust region preference approximation: A simple and stable reinforcement learning algorithm for llm reasoning. arXiv preprint arXiv:2504.04524, 2025.
[3] Yunyang Li, Lin Huang, Zhihao Ding, Chu Wang, Xinran Wei, Han Yang, Zun Wang, Chang Liu, Yu Shi, Peiran Jin, Jia Zhang, Mark Gerstein, Tao Qin. E2former: A linear-time efficient and equivariant transformer for scalable molecular modeling. arXiv e-prints, pages arXiv–2501, 2025.
[4] Mingqian Ma, Guoqing Liu, Chuan Cao, Pan Deng, Tri Dao, Albert Gu, Peiran Jin, Zhao Yang, Yingce Xia, Renqian Luo, et al. Hybridna: A hybrid transformer-mamba2 long-range dna language model. arXiv preprint arXiv:2502.10807, 2025.
[5] Liang He*, Peiran Jin*, Yaosen Min*, Shufang Xie*, Lijun Wu*, Tao Qin*, Xiaozhuan Liang, Kaiyuan Gao, Yuliang Jiang, Tie-Yan Liu. SFM-Protein: Integrative Co-evolutionary Pre-training for Advanced Protein Sequence Representation. arXiv, 2024.
[6] Yuxuan Ren, Dihan Zheng, Chang Liu, Peiran Jin, Yu Shi, Lin Huang, Jiyan He, Shengjie Luo, Tao Qin, and Tie-Yan Liu. Physical consistency bridges heterogeneous data in molecular multi-task learning. arXiv preprint arXiv:2410.10118, 2024.
[7] Shuxin Zheng, Jiyan He, Chang Liu, Yu Shi, Ziheng Lu, Weitao Feng, Fusong Ju, Jiaxi Wang, Jianwei Zhu, Yaosen Min, He Zhang, Shidi Tang, Hongxia Hao, Peiran Jin, Chi Chen, Frank Noé, Haiguang Liu and Tie-Yan Liu. Predicting equilibrium distributions for molecular systems with deep learning. Nature Machine Intelligence, 6(5):558–567, 2024.