Zobrazeno 1 - 5
of 5
pro vyhledávání: '"Huijie Zhen"'
Publikováno v:
Nanoscale. 14:2660-2667
Our Smooth Overlap of Atomic Positions-Machine Learning (SOAP-ML) model not only accelerates the DFT study but also makes a good prediction (MSE = 0.018) of the local atomic environment of a catalyst.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a24ba94b273ad980e5207ed1570e253c
https://doi.org/10.1039/d1nr07661b
https://doi.org/10.1039/d1nr07661b
Publikováno v:
The Journal of Physical Chemistry Letters. 12:1573-1580
Pt-Ni alloy nanoclusters are essential for high-performance catalysis, and the full description for the finite temperature properties is highly desired. Here we developed an efficient machine learning method to evaluate the accurate structure-stabili
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::56aca35cf8f753a6260877fd4c94c2f7
https://doi.org/10.1021/acs.jpclett.0c03600
https://doi.org/10.1021/acs.jpclett.0c03600
Autor:
Ye SONG, Huijie ZHENG, Lejing LIANG, Meizhen XU, Tianshi WU, Tinghuan HUANG, Yin PENG, Kaixing ZHU
Publikováno v:
Pifu-xingbing zhenliaoxue zazhi, Vol 31, Iss 7, Pp 458-463 (2024)
Objective To understand the epidemiological characteristics of genital Chlamydial infection in Haizhu district of Guangzhou from 2014 to 2023, and to provide a rationale for the development of intervention measures. Methods The epidemic data of genit
Externí odkaz:
https://doaj.org/article/9b080b5a925540119c91a80766c53e46
Autor:
Junjie Li, Junji Yuan, Deyan Liu, Xiaosong Zhao, Yanhong Dong, Huijie Zheng, Ye Li, Weixin Ding
Publikováno v:
Ecological Indicators, Vol 167, Iss , Pp 112663- (2024)
Wetland ecosystems play a pivotal role in terrestrial carbon and water cycles, thereby possessing great potential to regulate terrestrial water use efficiency (WUE), which is calculated as the ratio of gross primary productivity (GPP) to evapotranspi
Externí odkaz:
https://doaj.org/article/27f8a16e7f8745d68d1e7255a5ec53e0
Autor:
Xue Zhang, Georgios Chatzidrosos, Yinan Hu, Huijie Zheng, Arne Wickenbrock, Alexej Jerschow, Dmitry Budker
Publikováno v:
Applied Sciences, Vol 11, Iss 7, p 3069 (2021)
Sensitive and accurate diagnostic technologies with magnetic sensors are of great importance for identifying and localizing defects of rechargeable solid batteries using noninvasive detection. We demonstrate a microwave-free alternating current (AC)
Externí odkaz:
https://doaj.org/article/956cfc0ee12544579ec3f4447067daab