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pro vyhledávání: '"Nan, Bozhao"'
Autor:
Le, Khiem, Guo, Zhichun, Dong, Kaiwen, Huang, Xiaobao, Nan, Bozhao, Iyer, Roshni, Zhang, Xiangliang, Wiest, Olaf, Wang, Wei, Chawla, Nitesh V.
Large Language Models (LLMs) with their strong task-handling capabilities have shown remarkable advancements across a spectrum of fields, moving beyond natural language understanding. However, their proficiency within the chemistry domain remains res
Externí odkaz:
http://arxiv.org/abs/2406.06777
Autor:
Ma, Yihong, Huang, Xiaobao, Nan, Bozhao, Moniz, Nuno, Zhang, Xiangliang, Wiest, Olaf, Chawla, Nitesh V.
The yield of a chemical reaction quantifies the percentage of the target product formed in relation to the reactants consumed during the chemical reaction. Accurate yield prediction can guide chemists toward selecting high-yield reactions during synt
Externí odkaz:
http://arxiv.org/abs/2402.05971
Autor:
Guo, Taicheng, Ma, Changsheng, Chen, Xiuying, Nan, Bozhao, Guo, Kehan, Pei, Shichao, Chawla, Nitesh V., Wiest, Olaf, Zhang, Xiangliang
Reaction prediction, a critical task in synthetic chemistry, is to predict the outcome of a reaction based on given reactants. Generative models like Transformer and VAE have typically been employed to predict the reaction product. However, these lik
Externí odkaz:
http://arxiv.org/abs/2310.04674
Autor:
Zhu, Yanqiao, Hwang, Jeehyun, Adams, Keir, Liu, Zhen, Nan, Bozhao, Stenfors, Brock, Du, Yuanqi, Chauhan, Jatin, Wiest, Olaf, Isayev, Olexandr, Coley, Connor W., Sun, Yizhou, Wang, Wei
Molecular Representation Learning (MRL) has proven impactful in numerous biochemical applications such as drug discovery and enzyme design. While Graph Neural Networks (GNNs) are effective at learning molecular representations from a 2D molecular gra
Externí odkaz:
http://arxiv.org/abs/2310.00115
Autor:
Guo, Taicheng, Guo, Kehan, Nan, Bozhao, Liang, Zhenwen, Guo, Zhichun, Chawla, Nitesh V., Wiest, Olaf, Zhang, Xiangliang
Large Language Models (LLMs) with strong abilities in natural language processing tasks have emerged and have been applied in various kinds of areas such as science, finance and software engineering. However, the capability of LLMs to advance the fie
Externí odkaz:
http://arxiv.org/abs/2305.18365
Autor:
Guo, Zhichun, Guo, Kehan, Nan, Bozhao, Tian, Yijun, Iyer, Roshni G., Ma, Yihong, Wiest, Olaf, Zhang, Xiangliang, Wang, Wei, Zhang, Chuxu, Chawla, Nitesh V.
Molecular representation learning (MRL) is a key step to build the connection between machine learning and chemical science. In particular, it encodes molecules as numerical vectors preserving the molecular structures and features, on top of which th
Externí odkaz:
http://arxiv.org/abs/2207.04869
Autor:
Zhang, Runtong, Yan, Xin, Bai, Shao-Tao, Chen, Caiyou, Nan, Bozhao, Ma, Baode, Wen, Jialin, Zhang, Xumu
Publikováno v:
In Green Synthesis and Catalysis February 2022 3(1):40-45
Publikováno v:
In Tetrahedron 8 January 2022 104
Akademický článek
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Autor:
Guo, Taicheng, Guo, Kehan, Nan, Bozhao, Liang, Zhenwen, Guo, Zhichun, Chawla, Nitesh V., Wiest, Olaf, Zhang, Xiangliang
Large Language Models (LLMs) with strong abilities in natural language processing tasks have emerged and have been rapidly applied in various kinds of areas such as science, finance and software engineering. However, the capability of LLMs to advance
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c755fc64dc384987394880f194910500