Zobrazeno 1 - 10
of 100
pro vyhledávání: '"Wei, Xinran"'
Autor:
Ju, Fusong, Wei, Xinran, Huang, Lin, Jenkins, Andrew J., Xia, Leo, Zhang, Jia, Zhu, Jianwei, Yang, Han, Shao, Bin, Dai, Peggy, Mayya, Ashwin, Hooshmand, Zahra, Efimovskaya, Alexandra, Baker, Nathan A., Troyer, Matthias, Liu, Hongbin
Density functional theory (DFT) has been a cornerstone in computational chemistry, physics, and materials science for decades, benefiting from advancements in computational power and theoretical methods. This paper introduces a novel, cloud-native ap
Externí odkaz:
http://arxiv.org/abs/2406.11185
Autor:
Wang, Zun, Liu, Chang, Zou, Nianlong, Zhang, He, Wei, Xinran, Huang, Lin, Wu, Lijun, Shao, Bin
In this study, we introduce a unified neural network architecture, the Deep Equilibrium Density Functional Theory Hamiltonian (DEQH) model, which incorporates Deep Equilibrium Models (DEQs) for predicting Density Functional Theory (DFT) Hamiltonians.
Externí odkaz:
http://arxiv.org/abs/2406.03794
Autor:
Zhang, He, Liu, Chang, Wang, Zun, Wei, Xinran, Liu, Siyuan, Zheng, Nanning, Shao, Bin, Liu, Tie-Yan
Predicting the mean-field Hamiltonian matrix in density functional theory is a fundamental formulation to leverage machine learning for solving molecular science problems. Yet, its applicability is limited by insufficient labeled data for training. I
Externí odkaz:
http://arxiv.org/abs/2403.09560
Autor:
Qian, Kejiang, Mao, Lingjun, Liang, Xin, Ding, Yimin, Gao, Jin, Wei, Xinran, Guo, Ziyi, Li, Jiajie
In urban planning, land use readjustment plays a pivotal role in aligning land use configurations with the current demands for sustainable urban development. However, present-day urban planning practices face two main issues. Firstly, land use decisi
Externí odkaz:
http://arxiv.org/abs/2310.16772
Autor:
Wei, Xinran
In supervised machine learning, it is common practice to choose a loss function for learning predictive models, such as linear regression models and nonlinear neural networks. The primary objective is to attain accurate predictions. However, this can
Externí odkaz:
http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-513674
Autor:
Li, Yunyang, Wang, Yusong, Huang, Lin, Yang, Han, Wei, Xinran, Zhang, Jia, Wang, Tong, Wang, Zun, Shao, Bin, Liu, Tie-Yan
Computational simulation of chemical and biological systems using ab initio molecular dynamics has been a challenge over decades. Researchers have attempted to address the problem with machine learning and fragmentation-based methods. However, the tw
Externí odkaz:
http://arxiv.org/abs/2304.13542
Publikováno v:
Nanophotonics, Vol 13, Iss 21, Pp 3953-3961 (2024)
Hybridization coupling among plasmon modes is an effective approach to manipulate near-field properties thus optical spectral shapes of plasmonic nanostructures. Generally, mode hybridization coupling is achieved by modifying the topography and dimen
Externí odkaz:
https://doaj.org/article/1a4ab14123894ed79402042b6aa94f0c
Autor:
Huang, Xinquan, Shi, Wenlei, Gao, Xiaotian, Wei, Xinran, Zhang, Jia, Bian, Jiang, Yang, Mao, Liu, Tie-Yan
Publikováno v:
Neural Networks, 2024
Neural operators, as a powerful approximation to the non-linear operators between infinite-dimensional function spaces, have proved to be promising in accelerating the solution of partial differential equations (PDE). However, it requires a large amo
Externí odkaz:
http://arxiv.org/abs/2206.09418
Autor:
Huang, Xinquan, Shi, Wenlei, Gao, Xiaotian, Wei, Xinran, Zhang, Jia, Bian, Jiang, Yang, Mao, Liu, Tie-Yan
Publikováno v:
In Neural Networks August 2024 176
Autor:
Ma, Yirong, Wei, Xinran, Xu, Jiameng, Ji, Shuhua, Yang, Fan, Zeng, Aiguo, Li, Yunzhe, Cao, Jiliang, Zhang, Jia, Luo, Zhimin, Fu, Qiang
Publikováno v:
In Talanta 1 June 2024 273