Výsledky vyhledávání - "WANG Shaozhou"

Akademický článek

Coal mine drainage resources utilization technology innovation

Autor: HE Xuwen, WANG Shaozhou, ZHANG Xuewei, LI Fuqin

Publikováno v: Meitan kexue jishu, Vol 51, Iss 1, Pp 523-530 (2023)

The resource utilization of coal mine drainage has been attracting growing attention. The increasingly stringent discharge standards and the deepening of the concept of intelligent mine have posed new challenges to mine water treatment technology. Th

Externí odkaz: https://doaj.org/article/caecffee3a244b5ba7d58a7c7e915839

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Construction and Application of Materials Knowledge Graph in Multidisciplinary Materials Science via Large Language Model

Autor: Ye, Yanpeng, Ren, Jie, Wang, Shaozhou, Wan, Yuwei, Wang, Haofen, Razzak, Imran, Hoex, Bram, Xie, Tong, Zhang, Wenjie

Knowledge in materials science is widely dispersed across extensive scientific literature, posing significant challenges for efficient discovery and integration of new materials. Traditional methods, often reliant on costly and time-consuming experim

Externí odkaz: http://arxiv.org/abs/2404.03080

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DARWIN Series: Domain Specific Large Language Models for Natural Science

Autor: Xie, Tong, Wan, Yuwei, Huang, Wei, Yin, Zhenyu, Liu, Yixuan, Wang, Shaozhou, Linghu, Qingyuan, Kit, Chunyu, Grazian, Clara, Zhang, Wenjie, Razzak, Imran, Hoex, Bram

Emerging tools bring forth fresh approaches to work, and the field of natural science is no different. In natural science, traditional manual, serial, and labour-intensive work is being augmented by automated, parallel, and iterative processes driven

Externí odkaz: http://arxiv.org/abs/2308.13565

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Large Language Models as Master Key: Unlocking the Secrets of Materials Science with GPT

Autor: Xie, Tong, Wan, Yuwei, Huang, Wei, Zhou, Yufei, Liu, Yixuan, Linghu, Qingyuan, Wang, Shaozhou, Kit, Chunyu, Grazian, Clara, Zhang, Wenjie, Hoex, Bram

The amount of data has growing significance in exploring cutting-edge materials and a number of datasets have been generated either by hand or automated approaches. However, the materials science field struggles to effectively utilize the abundance o

Externí odkaz: http://arxiv.org/abs/2304.02213

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Interdisciplinary Discovery of Nanomaterials Based on Convolutional Neural Networks

Autor: Xie, Tong, Wan, Yuwei, Li, Weijian, Linghu, Qingyuan, Wang, Shaozhou, Cai, Yalun, Liu, Han, Kit, Chunyu, Grazian, Clara, Hoex, Bram

The material science literature contains up-to-date and comprehensive scientific knowledge of materials. However, their content is unstructured and diverse, resulting in a significant gap in providing sufficient information for material design and sy

Externí odkaz: http://arxiv.org/abs/2212.02805

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