Zobrazeno 1 - 9
of 9
pro vyhledávání: '"Qiuling Tao"'
Publikováno v:
npj Computational Materials, Vol 7, Iss 1, Pp 1-18 (2021)
Abstract The development of materials is one of the driving forces to accelerate modern scientific progress and technological innovation. Machine learning (ML) technology is rapidly developed in many fields and opening blueprints for the discovery an
Externí odkaz:
https://doaj.org/article/bf319000fe4f4a69bdf90cccef7de71a
Autor:
Liu Xiujuan, Xue Yang, Dongping Chang, Minjie Li, Wencong Lu, Long Li, Huimin Chen, Qiuling Tao, Tian Lu
Publikováno v:
The Journal of Physical Chemistry C. 125:21141-21150
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c9625cda6c5f255d44cf67e591e42dbb
https://doi.org/10.1021/acs.jpcc.1c05482
https://doi.org/10.1021/acs.jpcc.1c05482
Publikováno v:
Journal of Energy Chemistry. 60:351-359
Suffering from the inefficient traditional trial-and-error methods and the huge searching space filled by millions of candidates, discovering new perovskite visible photocatalysts with higher hydrogen production rate ( R H 2 ) still remains a challen
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7115687d555626df9d7f22d5ebb578de
https://doi.org/10.1016/j.jechem.2021.01.035
https://doi.org/10.1016/j.jechem.2021.01.035
Publikováno v:
The Journal of Physical Chemistry Letters. 12:7423-7430
Predicting the formability of perovskite structure for hybrid organic-inorganic perovskites (HOIPs) is a prominent challenge in the search for the required materials from a huge search space. Here, we propose an interpretable strategy combining machi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c817a9deb1c635a19b8123ca1fb4d10d
https://doi.org/10.1021/acs.jpclett.1c01939
https://doi.org/10.1021/acs.jpclett.1c01939
Publikováno v:
Computational Materials Science. 199:110712
Perovskite material attracts great interest in many sciences and engineering fields. It is meaningful to improve the accuracy of judging whether A B X 3 and A 2 B ' B ' ' X 6 compounds can form perovskite structures. In this work, machine learning me
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::30fd623add4a4b08565d5cc0790eed22
https://doi.org/10.1016/j.commatsci.2021.110712
https://doi.org/10.1016/j.commatsci.2021.110712
Publikováno v:
Computational Materials Science. 196:110528
It is urgent to discover new functional materials quickly, but experimental research is a huge challenge to search for target materials from the vast chemical space. Here, we propose a two-step machine learning strategy to accelerate the discovery of
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::eda88a0eb5906f13ffad0f279efd3bc3
https://doi.org/10.1016/j.commatsci.2021.110528
https://doi.org/10.1016/j.commatsci.2021.110528
Autor:
Qiuling Tao, Dongping Chang, Tian Lu, Long Li, Huimin Chen, Xue Yang, Xiujuan Liu, Minjie Li, Wencong Lu
Publikováno v:
Journal of Physical Chemistry C; 9/30/2021, Vol. 125 Issue 38, p21141-21150, 10p
Publikováno v:
NPJ Computational Materials; 1/29/2021, Vol. 7 Issue 1, p1-18, 18p
Autor:
Marcus Noack, Daniela Ushizima
Autonomous Experimentation is poised to revolutionize scientific experiments at advanced experimental facilities. Whereas previously, human experimenters were burdened with the laborious task of overseeing each measurement, recent advances in mathema
