Linfeng Zhang

According to our database, Linfeng Zhang authored at least 48 papers between 2017 and 2025.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2025

MolReasoner: Toward Effective and Interpretable Reasoning for Molecular LLMs.

Guojiang Zhao, Sihang Li, Zixiang Lu, Zheng Cheng, Haitao Lin, Lirong Wu, Hanchen Xia, Hengxing Cai, Wentao Guo, Hongshuai Wang, Mingjun Xu, Siyu Zhu, Guolin Ke, Linfeng Zhang, Zhifeng Gao

CoRR, August, 2025

SynBridge: Bridging Reaction States via Discrete Flow for Bidirectional Reaction Prediction.

Haitao Lin, Junjie Wang, Zhifeng Gao, Xiaohong Ji, Rong Zhu, Linfeng Zhang, Guolin Ke, Weinan E

CoRR, July, 2025

SciMaster: Towards General-Purpose Scientific AI Agents, Part I. X-Master as Foundation: Can We Lead on Humanity's Last Exam?

Jingyi Chai, Shuo Tang, Rui Ye, Yuwen Du, Xinyu Zhu, Mengcheng Zhou, Yanfeng Wang, Weinan E, Yuzhi Zhang, Linfeng Zhang, Siheng Chen

CoRR, July, 2025

Uni-AIMS: AI-Powered Microscopy Image Analysis.

Yanhui Hong, Nan Wang, Zhiyi Xia, Haoyi Tao, Xi Fang, Yiming Li, Jiankun Wang, Peng Jin, Xiaochen Cai, Shengyu Li, Ziqi Chen, Zezhong Zhang, Guolin Ke, Linfeng Zhang

CoRR, May, 2025

Toward a unified benchmark and framework for deep learning-based prediction of nuclear magnetic resonance chemical shifts.

Fanjie Xu, Wentao Guo, Feng Wang, Lin Yao, Hongshuai Wang, Fujie Tang, Zhifeng Gao, Linfeng Zhang, Weinan E, Zhong-Qun Tian, Jun Cheng

Nat. Comput. Sci., April, 2025

Uni-3DAR: Unified 3D Generation and Understanding via Autoregression on Compressed Spatial Tokens.

Shuqi Lu, Haowei Lin, Lin Yao, Zhifeng Gao, Xiaohong Ji, Weinan E, Linfeng Zhang, Guolin Ke

CoRR, March, 2025

Beyond Atoms: Enhancing Molecular Pretrained Representations with 3D Space Modeling.

Shuqi Lu, Xiaohong Ji, Bohang Zhang, Lin Yao, Siyuan Liu, Zhifeng Gao, Linfeng Zhang, Guolin Ke

CoRR, March, 2025

Strategic priorities for transformative progress in advancing biology with proteomics and artificial intelligence.

Yingying Sun, Jun A, Zhiwei Liu, Rui Sun, Liujia Qian, Samuel H. Payne, Wout Bittremieux, Markus Ralser, Chen Li, Yi Chen, Zhen Dong, Yasset Pérez-Riverol, Asif Khan, Chris Sander, Ruedi Aebersold, Juan Antonio Vizcaíno, Jonathan R. Krieger, Jianhua Yao, Han Wen, Linfeng Zhang, Yunping Zhu, Yue Xuan, Benjamin Boyang Sun, Liang Qiao, Henning Hermjakob, Haixu Tang, Huanhuan Gao, Yamin Deng, Qing Zhong, Cheng Chang, Nuno Bandeira, Ming Li, Weinan E, Siqi Sun, Yuedong Yang, Gilbert S. Omenn, Yue Zhang, Ping Xu, Yan Fu, Xiaowen Liu, Christopher M. Overall, Yu Wang, Eric W. Deutsch, Luonan Chen, Jürgen Cox, Vadim Demichev, Fuchu He, Jiaxing Huang, Huilin Jin, Chao Liu, Nan Li, Zhongzhi Luan, Jiangning Song, Kaicheng Yu, Wanggen Wan, Tai Wang, Kang Zhang, Le Zhang, Peter A. Bell, Matthias Mann, Bing Zhang, Tiannan Guo

CoRR, February, 2025

The OpenLAM Challenges.

Anyang Peng, Xinzijian Liu, Ming-Yu Guo, Linfeng Zhang, Han Wang

CoRR, January, 2025

SciAssess: Benchmarking LLM Proficiency in Scientific Literature Analysis.

Hengxing Cai, Xiaochen Cai, Junhan Chang, Sihang Li, Lin Yao, Changxin Wang, Zhifeng Gao, Hongshuai Wang, Yongge Li, Mujie Lin, Shuwen Yang, Jiankun Wang, Mingjun Xu, Jin Huang, Xi Fang, Jiaxi Zhuang, Yuqi Yin, Yaqi Li, Changhong Chen, Zheng Cheng, Zifeng Zhao, Linfeng Zhang, Guolin Ke

Proceedings of the Findings of the Association for Computational Linguistics: NAACL 2025, Albuquerque, New Mexico, USA, April 29, 2025

Learning local equivariant representations for quantum operators.

Zhanghao Zhouyin, Zixi Gan, Shishir Kumar Pandey, Linfeng Zhang, Qiangqiang Gu

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

SciLitLLM: How to Adapt LLMs for Scientific Literature Understanding.

Sihang Li, Jin Huang, Jiaxi Zhuang, Yaorui Shi, Xiaochen Cai, Mingjun Xu, Xiang Wang, Linfeng Zhang, Guolin Ke, Hengxing Cai

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

2024

Intelligent System for Automated Molecular Patent Infringement Assessment.

Yaorui Shi, Sihang Li, Taiyan Zhang, Xi Fang, Jiankun Wang, Zhiyuan Liu, Guojiang Zhao, Zhengdan Zhu, Zhifeng Gao, Renxin Zhong, Linfeng Zhang, Guolin Ke, Weinan E, Hengxing Cai, Xiang Wang

CoRR, 2024

MolParser: End-to-end Visual Recognition of Molecule Structures in the Wild.

Xi Fang, Jiankun Wang, Xiaochen Cai, Shangqian Chen, Shuwen Yang, Lin Yao, Linfeng Zhang, Guolin Ke

CoRR, 2024

Deep Learning Accelerated Quantum Transport Simulations in Nanoelectronics: From Break Junctions to Field-Effect Transistors.

Jijie Zou, Zhanghao Zhouyin, Dongying Lin, Linfeng Zhang, Shimin Hou, Qiangqiang Gu

CoRR, 2024

Uni-ELF: A Multi-Level Representation Learning Framework for Electrolyte Formulation Design.

Boshen Zeng, Sian Chen, Xinxin Liu, Changhong Chen, Bin Deng, Xiaoxu Wang, Zhifeng Gao, Yuzhi Zhang, Weinan E, Linfeng Zhang

CoRR, 2024

Uni-Mol2: Exploring Molecular Pretraining Model at Scale.

Xiaohong Ji, Zhen Wang, Zhifeng Gao, Hang Zheng, Linfeng Zhang, Guolin Ke, Weinan E

CoRR, 2024

Uni-Mol Docking V2: Towards Realistic and Accurate Binding Pose Prediction.

Eric Alcaide, Zhifeng Gao, Guolin Ke, Yaqi Li, Linfeng Zhang, Hang Zheng, Gengmo Zhou

CoRR, 2024

Dflow, a Python framework for constructing cloud-native AI-for-Science workflows.

Xinzijian Liu, Yanbo Han, Zhuoyuan Li, Jiahao Fan, Chengqian Zhang, Jinzhe Zeng, Yifan Shan, Yannan Yuan, Wei-Hong Xu, Yun-Pei Liu, Yuzhi Zhang, Tongqi Wen, Darrin M. York, Zhicheng Zhong, Hang Zheng, Jun Cheng, Linfeng Zhang, Han Wang

CoRR, 2024

Uni-SMART: Universal Science Multimodal Analysis and Research Transformer.

Hengxing Cai, Xiaochen Cai, Shuwen Yang, Jiankun Wang, Lin Yao, Zhifeng Gao, Junhan Chang, Sihang Li, Mingjun Xu, Changxin Wang, Hongshuai Wang, Yongge Li, Mujie Lin, Yaqi Li, Yuqi Yin, Linfeng Zhang, Guolin Ke

CoRR, 2024

SciAssess: Benchmarking LLM Proficiency in Scientific Literature Analysis.

Hengxing Cai, Xiaochen Cai, Junhan Chang, Sihang Li, Lin Yao, Changxin Wang, Zhifeng Gao, Hongshuai Wang, Yongge Li, Mujie Lin, Shuwen Yang, Jiankun Wang, Yuqi Yin, Yaqi Li, Linfeng Zhang, Guolin Ke

CoRR, 2024

Exploring Molecular Pretraining Model at Scale.

Xiaohong Ji, Zhen Wang, Zhifeng Gao, Hang Zheng, Linfeng Zhang, Guolin Ke, Weinan E

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

2023

Scientific discovery in the age of artificial intelligence.

Hanchen Wang, Tianfan Fu, Yuanqi Du, Wenhao Gao, Kexin Huang, Ziming Liu, Payal Chandak, Shengchao Liu, Peter Van Katwyk, Andreea Deac, Anima Anandkumar, Karianne Bergen, Carla P. Gomes, Shirley Ho, Pushmeet Kohli, Joan Lasenby, Jure Leskovec, Tie-Yan Liu, Arjun Manrai, Debora S. Marks, Bharath Ramsundar, Le Song, Jimeng Sun, Jian Tang, Petar Velickovic, Max Welling, Linfeng Zhang, Connor W. Coley, Yoshua Bengio, Marinka Zitnik

Nat., 2023

DeePKS-kit: A package for developing machine learning-based chemically accurate energy and density functional models.

Yixiao Chen, Linfeng Zhang, Han Wang, Weinan E

Comput. Phys. Commun., 2023

Uni-QSAR: an Auto-ML Tool for Molecular Property Prediction.

Zhifeng Gao, Xiaohong Ji, Guojiang Zhao, Hongshuai Wang, Hang Zheng, Guolin Ke, Linfeng Zhang

CoRR, 2023

Highly Accurate Quantum Chemical Property Prediction with Uni-Mol+.

Shuqi Lu, Zhifeng Gao, Di He, Linfeng Zhang, Guolin Ke

CoRR, 2023

Uni-Mol: A Universal 3D Molecular Representation Learning Framework.

Gengmo Zhou, Zhifeng Gao, Qiankun Ding, Hang Zheng, Hongteng Xu, Zhewei Wei, Linfeng Zhang, Guolin Ke

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

Efficient sampling of high-dimensional free energy landscapes using adaptive reinforced dynamics.

Dongdong Wang, Yanze Wang, Junhan Chang, Linfeng Zhang, Han Wang, Weinan E

Nat. Comput. Sci., 2022

A deep variational free energy approach to dense hydrogen.

Hao Xie, Zi-Hang Li, Han Wang, Linfeng Zhang, Lei Wang

CoRR, 2022

DPA-1: Pretraining of Attention-based Deep Potential Model for Molecular Simulation.

Duo Zhang, Hangrui Bi, Fu-Zhi Dai, Wanrun Jiang, Linfeng Zhang, Han Wang

CoRR, 2022

DeePKS+ABACUS as a Bridge between Expensive Quantum Mechanical Models and Machine Learning Potentials.

Wenfei Li, Qi Ou, Yixiao Chen, Yu Cao, Renxi Liu, Chunyi Zhang, Daye Zheng, Chun Cai, Xifan Wu, Han Wang, Mohan Chen, Linfeng Zhang

CoRR, 2022

Extending the limit of molecular dynamics with ab initio accuracy to 10 billion atoms.

Zhuoqiang Guo, Denghui Lu, Yujin Yan, Siyu Hu, Rongrong Liu, Guangming Tan, Ninghui Sun, Wanrun Jiang, Lijun Liu, Yixiao Chen, Linfeng Zhang, Mohan Chen, Han Wang, Weile Jia

CoRR, 2022

Extending the limit of molecular dynamics with <i>ab initio</i> accuracy to 10 billion atoms.

Zhuoqiang Guo, Denghui Lu, Yujin Yan, Siyu Hu, Rongrong Liu, Guangming Tan, Ninghui Sun, Wanrun Jiang, Lijun Liu, Yixiao Chen, Linfeng Zhang, Mohan Chen, Han Wang, Weile Jia

Proceedings of the PPoPP '22: 27th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, Seoul, Republic of Korea, April 2, 2022

2021

Deep Density: Circumventing the Kohn-Sham equations via symmetry preserving neural networks.

Leonardo Zepeda-Núñez, Yixiao Chen, Jiefu Zhang, Weile Jia, Linfeng Zhang, Lin Lin

J. Comput. Phys., 2021

86 PFLOPS Deep Potential Molecular Dynamics simulation of 100 million atoms with <i>ab initio</i> accuracy.

Denghui Lu, Han Wang, Mohan Chen, Lin Lin, Roberto Car, Weinan E, Weile Jia, Linfeng Zhang

Comput. Phys. Commun., 2021

2020

DP-GEN: A concurrent learning platform for the generation of reliable deep learning based potential energy models.

Yuzhi Zhang, Haidi Wang, Weijie Chen, Jinzhe Zeng, Linfeng Zhang, Han Wang, Weinan E

Comput. Phys. Commun., 2020

DeePKS: a comprehensive data-driven approach towards chemically accurate density functional theory.

Yixiao Chen, Linfeng Zhang, Han Wang, Weinan E

CoRR, 2020

Integrating Machine Learning with Physics-Based Modeling.

Weinan E, Jiequn Han, Linfeng Zhang

CoRR, 2020

86 PFLOPS Deep Potential Molecular Dynamics simulation of 100 million atoms with ab initio accuracy.

Denghui Lu, Han Wang, Mohan Chen, Jiduan Liu, Lin Lin, Roberto Car, Weinan E, Weile Jia, Linfeng Zhang

CoRR, 2020

Pushing the limit of molecular dynamics with <i>ab initio</i> accuracy to 100 million atoms with machine learning.

Weile Jia, Han Wang, Mohan Chen, Denghui Lu, Lin Lin, Roberto Car, Weinan E, Linfeng Zhang

Proceedings of the International Conference for High Performance Computing, 2020

2019

Solving many-electron Schrödinger equation using deep neural networks.

Jiequn Han, Linfeng Zhang, Weinan E

J. Comput. Phys., 2019

Universal approximation of symmetric and anti-symmetric functions.

Jiequn Han, Yingzhou Li, Lin Lin, Jianfeng Lu, Jiefu Zhang, Linfeng Zhang

CoRR, 2019

2018

DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics.

Han Wang, Linfeng Zhang, Jiequn Han, Weinan E

Comput. Phys. Commun., 2018

Active Learning of Uniformly Accurate Inter-atomic Potentials for Materials Simulation.

Linfeng Zhang, De-Ye Lin, Han Wang, Roberto Car, Weinan E

CoRR, 2018

Monge-Ampère Flow for Generative Modeling.

Linfeng Zhang, Weinan E, Lei Wang

CoRR, 2018

End-to-end Symmetry Preserving Inter-atomic Potential Energy Model for Finite and Extended Systems.

Linfeng Zhang, Jiequn Han, Han Wang, Wissam Saidi, Roberto Car, Weinan E

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

2017

Reinforced dynamics for enhanced sampling in large atomic and molecular systems. I. Basic Methodology.

Linfeng Zhang, Han Wang, Weinan E

CoRR, 2017

Deep Potential Molecular Dynamics: a scalable model with the accuracy of quantum mechanics.

Linfeng Zhang, Jiequn Han, Han Wang, Roberto Car, Weinan E

CoRR, 2017