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pro vyhledávání: '"Chang, Junhan"'
Autor:
Chang, Junhan, Zhang, Duo, Deng, Yuqing, Lin, Hongrui, Liu, Zhirong, Zhang, Linfeng, Zheng, Hang, Wang, Xinyan
Molecular simulations are essential tools in computational chemistry, enabling the prediction and understanding of molecular interactions and thermodynamic properties of biomolecules. However, traditional force fields face significant challenges in a
Externí odkaz:
http://arxiv.org/abs/2406.09817
Autor:
Cai, Hengxing, Cai, Xiaochen, Yang, Shuwen, Wang, Jiankun, Yao, Lin, Gao, Zhifeng, Chang, Junhan, Li, Sihang, Xu, Mingjun, Wang, Changxin, Wang, Hongshuai, Li, Yongge, Lin, Mujie, Li, Yaqi, Yin, Yuqi, Zhang, Linfeng, Ke, Guolin
In scientific research and its application, scientific literature analysis is crucial as it allows researchers to build on the work of others. However, the fast growth of scientific knowledge has led to a massive increase in scholarly articles, makin
Externí odkaz:
http://arxiv.org/abs/2403.10301
Autor:
Cai, Hengxing, Cai, Xiaochen, Chang, Junhan, Li, Sihang, Yao, Lin, Wang, Changxin, Gao, Zhifeng, Wang, Hongshuai, Li, Yongge, Lin, Mujie, Yang, Shuwen, Wang, Jiankun, Xu, Mingjun, Huang, Jin, Fang, Xi, Zhuang, Jiaxi, Yin, Yuqi, Li, Yaqi, Chen, Changhong, Cheng, Zheng, Zhao, Zifeng, Zhang, Linfeng, Ke, Guolin
Recent breakthroughs in Large Language Models (LLMs) have revolutionized scientific literature analysis. However, existing benchmarks fail to adequately evaluate the proficiency of LLMs in this domain, particularly in scenarios requiring higher-level
Externí odkaz:
http://arxiv.org/abs/2403.01976
Autor:
Zhang, Duo, Liu, Xinzijian, Zhang, Xiangyu, Zhang, Chengqian, Cai, Chun, Bi, Hangrui, Du, Yiming, Qin, Xuejian, Peng, Anyang, Huang, Jiameng, Li, Bowen, Shan, Yifan, Zeng, Jinzhe, Zhang, Yuzhi, Liu, Siyuan, Li, Yifan, Chang, Junhan, Wang, Xinyan, Zhou, Shuo, Liu, Jianchuan, Luo, Xiaoshan, Wang, Zhenyu, Jiang, Wanrun, Wu, Jing, Yang, Yudi, Yang, Jiyuan, Yang, Manyi, Gong, Fu-Qiang, Zhang, Linshuang, Shi, Mengchao, Dai, Fu-Zhi, York, Darrin M., Liu, Shi, Zhu, Tong, Zhong, Zhicheng, Lv, Jian, Cheng, Jun, Jia, Weile, Chen, Mohan, Ke, Guolin, E, Weinan, Zhang, Linfeng, Wang, Han
The rapid advancements in artificial intelligence (AI) are catalyzing transformative changes in atomic modeling, simulation, and design. AI-driven potential energy models have demonstrated the capability to conduct large-scale, long-duration simulati
Externí odkaz:
http://arxiv.org/abs/2312.15492
Autor:
Zeng, Jinzhe, Zhang, Duo, Lu, Denghui, Mo, Pinghui, Li, Zeyu, Chen, Yixiao, Rynik, Marián, Huang, Li'ang, Li, Ziyao, Shi, Shaochen, Wang, Yingze, Ye, Haotian, Tuo, Ping, Yang, Jiabin, Ding, Ye, Li, Yifan, Tisi, Davide, Zeng, Qiyu, Bao, Han, Xia, Yu, Huang, Jiameng, Muraoka, Koki, Wang, Yibo, Chang, Junhan, Yuan, Fengbo, Bore, Sigbjørn Løland, Cai, Chun, Lin, Yinnian, Wang, Bo, Xu, Jiayan, Zhu, Jia-Xin, Luo, Chenxing, Zhang, Yuzhi, Goodall, Rhys E. A., Liang, Wenshuo, Singh, Anurag Kumar, Yao, Sikai, Zhang, Jingchao, Wentzcovitch, Renata, Han, Jiequn, Liu, Jie, Jia, Weile, York, Darrin M., E, Weinan, Car, Roberto, Zhang, Linfeng, Wang, Han
Publikováno v:
J. Chem. Phys. 159, 054801 (2023)
DeePMD-kit is a powerful open-source software package that facilitates molecular dynamics simulations using machine learning potentials (MLP) known as Deep Potential (DP) models. This package, which was released in 2017, has been widely used in the f
Externí odkaz:
http://arxiv.org/abs/2304.09409
Enhanced sampling methods such as metadynamics and umbrella sampling have become essential tools for exploring the configuration space of molecules and materials. At the same time, they have long faced a number of issues such as the inefficiency when
Externí odkaz:
http://arxiv.org/abs/2104.01620
Autor:
Zhang, Jun, Lei, Yao-Kun, Zhang, Zhen, Chang, Junhan, Li, Maodong, Han, Xu, Yang, Lijiang, Yang, Yi Isaac, Gao, Yi Qin
Publikováno v:
J.Phys.Chem.A,2020,124,34,6745-6763
Deep learning is transforming many areas in science, and it has great potential in modeling molecular systems. However, unlike the mature deployment of deep learning in computer vision and natural language processing, its development in molecular mod
Externí odkaz:
http://arxiv.org/abs/2004.13011
Akademický článek
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Autor:
Zhang, Jun, Lei, Yao-Kun, Zhang, Zhen, Chang, Junhan, Li, Maodong, Han, Xu, Yang, Lijiang, Yang, Yi Isaac, Gao, Yi Qin
Publikováno v:
The Journal of Physical Chemistry - Part B; 20240101, Issue: Preprints
Autor:
Wang D; Program in Applied and Computational Mathematics, Princeton University, Princeton, NJ, USA.; DP Technology, Beijing, People's Republic of China., Wang Y; DP Technology, Beijing, People's Republic of China.; College of Chemistry and Molecular Engineering, Peking University, Beijing, People's Republic of China., Chang J; DP Technology, Beijing, People's Republic of China.; College of Chemistry and Molecular Engineering, Peking University, Beijing, People's Republic of China., Zhang L; Program in Applied and Computational Mathematics, Princeton University, Princeton, NJ, USA. linfeng.zhang.zlf@gmail.com.; DP Technology, Beijing, People's Republic of China. linfeng.zhang.zlf@gmail.com., Wang H; Laboratory of Computational Physics, Institute of Applied Physics and Computational Mathematics, Beijing, People's Republic of China. wang_han@iapcm.ac.cn., E W; School of Mathematical Sciences, Peking University, Beijing, People's Republic of China.; Department of Mathematics and Program in Applied and Computational Mathematics, Princeton University, Princeton, NJ, USA.; Beijing Institute of Big Data Research, Beijing, People's Republic of China.
Publikováno v:
Nature computational science [Nat Comput Sci] 2022 Jan; Vol. 2 (1), pp. 20-29. Date of Electronic Publication: 2021 Dec 24.