Zobrazeno 1 - 10
of 33
pro vyhledávání: '"Computational method in materials science"'
Publikováno v:
iScience, Vol 26, Iss 7, Pp 107029- (2023)
Summary: Modern heterogeneous catalysis has benefitted immensely from computational predictions of catalyst structure and its evolution under reaction conditions, first-principles mechanistic investigations, and detailed kinetic modeling, which are r
Externí odkaz:
https://doaj.org/article/63b55adf95c149e4bde3cb1190bcd656
Publikováno v:
iScience, Vol 25, Iss 1, Pp 103543- (2022)
Summary: For electrochemical hydrogen evolution reaction (HER), developing high-performance catalysts without containing precious metals have been a major research focus in the present. Herein, we show the feasibility of HER catalytic enhancement in
Externí odkaz:
https://doaj.org/article/d5315e1ff88e47959c1289c77f93ccb2
Publikováno v:
iScience, Vol 24, Iss 12, Pp 103430- (2021)
Summary: To achieve net-zero emissions, a particular interest has been raised in the electrochemical evolution of H2 by using catalysts. Considering the complexity of designing catalyst, we demonstrate a data-driven strategy to develop optimized cata
Externí odkaz:
https://doaj.org/article/72e7cc013ec844c8a363cf87bd77aede
Autor:
Mario Milazzo, Markus J. Buehler
Publikováno v:
iScience, Vol 24, Iss 8, Pp 102873- (2021)
Summary: Fire has fascinated humankind since the prehistoric era. Rooted in the interactions between sound and flames, here we report a method to use fire for a variety of purposes, including sonification, art, and the design and manufacturing nature
Externí odkaz:
https://doaj.org/article/9cfaa2bdec064dba8c64b5c27f174bfb
Autor:
Aldair E. Gongora, Kelsey L. Snapp, Emily Whiting, Patrick Riley, Kristofer G. Reyes, Elise F. Morgan, Keith A. Brown
Publikováno v:
iScience, Vol 24, Iss 4, Pp 102262- (2021)
Summary: Autonomous experimentation (AE) accelerates research by combining automation and machine learning to perform experiments intelligently and rapidly in a sequential fashion. While AE systems are most needed to study properties that cannot be p
Externí odkaz:
https://doaj.org/article/fe4cdcd285994736a58c67f60e0615ef
Autor:
Xiaona Huang, Chongyang Li, Kaiyuan Tan, Yushi Wen, Feng Guo, Ming Li, Yongli Huang, Chang Q. Sun, Michael Gozin, Lei Zhang
Publikováno v:
iScience, Vol 24, Iss 3, Pp 102240- (2021)
Summary: The long-standing performance-stability contradiction issue of high energy density materials (HEDMs) is of extremely complex and multi-parameter nature. Herein, machine learning was employed to handle 28 feature descriptors and 5 properties
Externí odkaz:
https://doaj.org/article/896b6df7aa094999a5e7f88b446fc0ee
Publikováno v:
iScience
iScience, Vol 24, Iss 12, Pp 103430-(2021)
iScience, Vol 24, Iss 12, Pp 103430-(2021)
Summary To achieve net-zero emissions, a particular interest has been raised in the electrochemical evolution of H2 by using catalysts. Considering the complexity of designing catalyst, we demonstrate a data-driven strategy to develop optimized catal
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3b3e9b3d0cb2a4dcee5a35b7df499c34
http://europepmc.org/articles/PMC8637634
http://europepmc.org/articles/PMC8637634
Autor:
Chongyang Li, Yushi Wen, Chang Q. Sun, Feng Guo, Kaiyuan Tan, Ming Li, Xiaona Huang, Michael Gozin, Yongli Huang, Lei Zhang
Publikováno v:
iScience, Vol 24, Iss 3, Pp 102240-(2021)
iScience
iScience
Summary The long-standing performance-stability contradiction issue of high energy density materials (HEDMs) is of extremely complex and multi-parameter nature. Herein, machine learning was employed to handle 28 feature descriptors and 5 properties o
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2d5b8a59a6054ec635f321dc78ecf23c
http://www.sciencedirect.com/science/article/pii/S258900422100208X
http://www.sciencedirect.com/science/article/pii/S258900422100208X
Autor:
Keith A. Brown, Aldair E. Gongora, Kelsey L. Snapp, Emily Whiting, Patrick Riley, Kristofer G. Reyes, Elise F. Morgan
Publikováno v:
iScience
iScience, Vol 24, Iss 4, Pp 102262-(2021)
iScience, Vol 24, Iss 4, Pp 102262-(2021)
Summary Autonomous experimentation (AE) accelerates research by combining automation and machine learning to perform experiments intelligently and rapidly in a sequential fashion. While AE systems are most needed to study properties that cannot be pr
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e73a39cbe8e7cf593fe111a29d6233a9
http://europepmc.org/articles/PMC8010472
http://europepmc.org/articles/PMC8010472
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