Zobrazeno 1 - 10
of 700
pro vyhledávání: '"materials discovery"'
Publikováno v:
Computational and Structural Biotechnology Journal, Vol 25, Iss , Pp 186-193 (2024)
Introduction: The quest to develop efficient, sustainable materials from non-critical, non-toxic resources is one of today's most formidable challenges in the current context of energy, transport, digital or healthcare transitions. In response, Franc
Externí odkaz:
https://doaj.org/article/fb84f63c9c334b52a906afdf3113e5c2
Autor:
Susumu Fujii, Junji Hyodo, Kazuki Shitara, Akihide Kuwabara, Shusuke Kasamatsu, Yoshihiro Yamazaki
Publikováno v:
Science and Technology of Advanced Materials, Vol 25, Iss 1 (2024)
This review presents computational and machine learning methodologies developed during a 5-year research project on proton-conducting oxides. The main goal was to develop methodologies that could assist in materials discovery or provide new insights
Externí odkaz:
https://doaj.org/article/66702fbd92e5461292685b2189a68796
Autor:
Hocheol Lim
Publikováno v:
Materials Today Advances, Vol 24, Iss , Pp 100529- (2024)
Greenhouse gas emissions, particularly carbon dioxide (CO2), pose a significant threat to the climate, driving global warming and numerous environmental issues. Ionic liquids (ILs) have gained attention for CO2 capture due to their tunable properties
Externí odkaz:
https://doaj.org/article/3f3d63747c164dc08aa72a63b621c7f4
Publikováno v:
Nano Trends, Vol 8, Iss , Pp 100052- (2024)
The convergence of Artificial Intelligence (AI) and nanotechnology is a transformative frontier, holding vast potential for scientific and technological advancements. This review explores the integration of AI and nanotechnology, aiming to uncover cu
Externí odkaz:
https://doaj.org/article/84f40f2497e148f9bc0c3bed2a73d4f6
Publikováno v:
Science and Technology of Advanced Materials: Methods, Vol 4, Iss 1 (2024)
ABSTRACTThe acceleration of materials discovery has gained paramount importance due to its potential to overcome constraints in emerging technologies. Extensive exploration has been undertaken into three pivotal approaches: combinatorial synthesis, h
Externí odkaz:
https://doaj.org/article/08be825a70044b0faf8f1adff17f8b81
Akademický článek
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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:
ChemElectroChem, Vol 11, Iss 15, Pp n/a-n/a (2024)
Abstract This review exploits the crucial role of computational methods in discovering and optimizing materials for redox flow batteries (RFBs). Integration of high‐throughput computational screening (HTCS) and machine learning (ML) accelerates mat
Externí odkaz:
https://doaj.org/article/5717a7335bd04a3881cfe6165ae11d23
Publikováno v:
Advanced Intelligent Systems, Vol 6, Iss 7, Pp n/a-n/a (2024)
Machine learning (ML) has been harnessed as a promising modelling tool for materials research. However, small data, or data scarcity, is a bottleneck when incorporating ML in studies involving experimentation. Current experiment planning methods show
Externí odkaz:
https://doaj.org/article/f8283f82bcb548bd86a83e6e39018423
Publikováno v:
Artificial Intelligence Chemistry, Vol 2, Iss 1, Pp 100045- (2024)
Synchrotron radiation technology provides high-resolution and high-sensitivity information for many fields such as material science, life science, and energy research. Synchrotron radiation data-driven methods have significantly accelerated the devel
Externí odkaz:
https://doaj.org/article/8b37534438904766ae9a3661dc0564db