Zobrazeno 1 - 10
of 42
pro vyhledávání: '"Rongzhi Dong"'
Publikováno v:
npj Computational Materials, Vol 10, Iss 1, Pp 1-14 (2024)
Abstract In real-world materials research, machine learning (ML) models are usually expected to predict and discover novel exceptional materials that deviate from the known materials. It is thus a pressing question to provide an objective evaluation
Externí odkaz:
https://doaj.org/article/9a0006572cef4d039baef768e026a774
Autor:
Lai Wei, Qinyang Li, Yuqi Song, Stanislav Stefanov, Rongzhi Dong, Nihang Fu, Edirisuriya M. D. Siriwardane, Fanglin Chen, Jianjun Hu
Publikováno v:
Advanced Science, Vol 11, Iss 36, Pp n/a-n/a (2024)
Abstract Self‐supervised neural language models have recently achieved unprecedented success from natural language processing to learning the languages of biological sequences and organic molecules. These models have demonstrated superior performan
Externí odkaz:
https://doaj.org/article/de0976dc7edb485c9a7f491c7061ce9c
Publikováno v:
Advanced Intelligent Systems, Vol 5, Iss 12, Pp n/a-n/a (2023)
Two‐dimensional (2D) materials offer great potential in various fields like superconductivity, quantum systems, and topological materials. However, designing them systematically remains challenging due to the limited pool of fewer than 100 experime
Externí odkaz:
https://doaj.org/article/d1bea25de4d84e5eb895cda8f1540f9a
Publikováno v:
ACS Omega, Vol 6, Iss 17, Pp 11585-11594 (2021)
Externí odkaz:
https://doaj.org/article/13f37c5bd5db454b897ee1f4c7198564
Autor:
Sadman Sadeed Omee, Steph-Yves Louis, Nihang Fu, Lai Wei, Sourin Dey, Rongzhi Dong, Qinyang Li, Jianjun Hu
Publikováno v:
Patterns, Vol 3, Iss 5, Pp 100491- (2022)
Summary: Machine-learning-based materials property prediction models have emerged as a promising approach for new materials discovery, among which the graph neural networks (GNNs) have shown the best performance due to their capability to learn high-
Externí odkaz:
https://doaj.org/article/f6b671582e3a4ea998b3f9649ccb956e
Publikováno v:
ACS Omega, Vol 5, Iss 7, Pp 3596-3606 (2020)
Externí odkaz:
https://doaj.org/article/d4e4439d6b924d34a18d604b8fb65eca
Publikováno v:
IEEE Access, Vol 8, Pp 57868-57878 (2020)
Superconductors have been one of the most intriguing materials since they were discovered more than a century ago. However, superconductors at room temperature have yet to be discovered. On the other hand, machine learning and especially deep learnin
Externí odkaz:
https://doaj.org/article/7567d53f228e4786965f17e315d07898
Autor:
Nihang Fu, Lai Wei, Yuqi Song, Qinyang Li, Rui Xin, Sadman Sadeed Omee, Rongzhi Dong, Edirisuriya M Dilanga Siriwardane, Jianjun Hu
Publikováno v:
Machine Learning: Science and Technology, Vol 4, Iss 1, p 015001 (2023)
Pre-trained transformer language models (LMs) on large unlabeled corpus have produced state-of-the-art results in natural language processing, organic molecule design, and protein sequence generation. However, no such models have been applied to lear
Externí odkaz:
https://doaj.org/article/ddaf5bad39254ce1bbd45bc57cb6cca6
Publikováno v:
IEEE Access, Vol 7, Pp 110895-110904 (2019)
This paper focuses on bearing fault diagnosis with limited training data. A major challenge in fault diagnosis is the infeasibility of obtaining sufficient training samples for every fault type under all working conditions. Recently deep learning bas
Externí odkaz:
https://doaj.org/article/ef09c28003404236be7dfa0ad09057fb
Autor:
Lai Wei, Nihang Fu, Edirisuriya M. D. Siriwardane, Wenhui Yang, Sadman Sadeed Omee, Rongzhi Dong, Rui Xin, Jianjun Hu
Publikováno v:
Inorganic Chemistry. 61:8431-8439
Fast and accurate crystal structure prediction (CSP) algorithms and web servers are highly desirable for the exploration and discovery of new materials out of the infinite chemical design space. However, currently, the computationally expensive first
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::55ef03533888544177da04d94f659964
https://doi.org/10.1021/acs.inorgchem.1c03879
https://doi.org/10.1021/acs.inorgchem.1c03879