374 research outputs found
Source code and demo of MAGUS (Machine Learning and Graph Theory Assisted Universal Structure Searcher) v2.0.0
No full textThis is the source code and demo for MAGUS (Machine Learning and Graph Theory Assisted Universal Structure Searcher) v2.0.0. Future updates can be accessed from gitlab (https://gitlab.com/bigd4/magus) after registration (https://www.wjx.top/vm/m5eWS0X.aspx).MAGUS is a Python package designed to predict crystal structures, which is free for non-commercial academic use, subject to registration and approval at https://www.wjx.top/vm/m5eWS0X.aspx .
1) This Code or its derivative work will not be used for any purpose other than non-commercial research.
2) This Code or its derivative code will not be published or otherwise distributed.
3) The use of this Code should be acknowledged by citing the tutorial paper describing the use of this code:
[1] Junjie Wang, Hao Gao, Yu Han, Chi Ding, Shuning Pan, Yong Wang, Qiuhan Jia, Hui-Tian Wang, Dingyu Xing, and Jian Sun, "MAGUS: machine learning and graph theory assisted universal structure searcher", National Science Review 10 (7), nwad128 (2023).
[2] Kang Xia, Hao Gao, Cong Liu, Jianan Yuan, Jian Sun, Hui-Tian Wang, Dingyu Xing, "A novel superhard tungsten nitride predicted by machine-learning accelerated crystal structure search", Sci. Bull. 63, 817 (2018).Additional references that may be cited (at the discretion of the user) are:
Graph theory:
[3] Hao Gao, Junjie Wang, Yu Han, Jian Sun, "Enhancing Crystal Structure Prediction by Decomposition and Evolution Schemes Based on Graph Theory", Fundamental Research 1, 466 (2021).
[4] Hao Gao, Junjie Wang, Zhaopeng Guo, Jian Sun, "Determining dimensionalities and multiplicities of crystal nets" npj Comput. Mater. 6, 143 (2020).
Surface reconstruction:
[5] Y. Han, J. Wang, C. Ding, H. Gao, S. Pan, Q. Jia, and J. Sun, "Prediction of surface reconstructions using MAGUS", The Journal of Chemical Physics 158 (17), 174109 (2023).
Structure searching in confined space:
[6] Chi Ding, Junjie Wang, Yu Han, Jianan Yuan, Hao Gao, and Jian Sun, "High Energy Density Polymeric Nitrogen Nanotubes inside Carbon Nanotubes", Chin. Phys. Lett. 39, 036101 (2022). (Express Letter)Peer reviewe
SALIENCY DETECTION VIA GLOBAL-OBJECT-SEED-GUIDED CELLULAR AUTOMATA
No full textImage saliency detection has attracted much attention in recent years, while several challenging problems are still unsolved, such as inaccurate saliency detection in complex scenes and suppressing salient objects near image borders. In this paper, a novel algorithm is proposed to solve these problems. Firstly, we collect background seeds from image borders by boundary information and construct a background based saliency map via low level features. Then, a novel propagation mechanism named global-object-seed-guided Cellular Automata model is builded. Cellular Automata exploits the intrinsic relevance of similar regions through interactions with neighbors, and global object seeds reduce the difference between dissimilar adjacent regions in the whole salient object. Experimental results on public benchmark datasets demonstrate the superiority of the proposed algorithm over ten state-of-the-art saliency models.CPCI-S(ISTP)[email protected]; [email protected]; [email protected]
Neoliberal global assemblages: The emergence of “public” international high-school curriculum programs in China
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Neoliberal global assemblages: the emergence of “public” international high-school curriculum programs in China
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“New” parental choice of school in China?: the choice of international high-school curriculum programs
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