Synchrotron radiation data-driven artificial intelligence approaches in materials discovery

Li, Qingmeng; Xing, Rongchang; Li, Linshan; Yao, Haodong; Wu, Liyuan; Zhao, Lina

doi:10.1016/j.aichem.2024.100045

Artificial Intelligence Chemistry

2024

DOI: 10.1016/j.aichem.2024.100045

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Synchrotron radiation data-driven artificial intelligence approaches in materials discovery

Qingmeng Li,

Rongchang Xing,

Linshan Li

et al.

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2024

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Cited by 3 publications

References 70 publications

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Multidisciplinary and multiscale nanoscience research roadmap based on large scientific facilities

Zeng,

Zhang,

Guo

et al. 2024

Sci. China Chem.

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Multidisciplinary and multiscale nanoscience research roadmap based on large scientific facilities

Zeng,

Zhang,

Guo

et al. 2024

Sci. China Chem.

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In situ/Operando Investigation for Heterogeneous Electro-Catalysts: From Model Catalysts to State-of-the-Art Catalysts

Song,

Qian,

Yang

et al. 2024

ACS Energy Lett.

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Electrochemical reactions, including water splitting, oxygen reduction, hydrogen oxidation, carbon dioxide reduction, nitrogen oxide reduction, etc., are critical for sustainable energy conversion and storage. Achieving high efficiency in these reactions requires catalysts with superior activity, selectivity, and stability, often realized through nanostructured metal catalysts. However, practical challenges such as low selectivity and catalytic degradation persist. In situ and operando characterization techniques offer real-time insights into catalyst behavior under reaction conditions, enabling a deeper understanding of structure−performance relationships and, therefore, guiding the design and optimization of electro-catalysts. This review discusses the common in situ/operando techniques, highlights their applications in model catalysts, including single-atom and single-crystal catalysts, and further explores their combinational analysis to study practical complex nanocatalysts. Finally, we provide suggestions and perspectives on the development of the in situ/operando techniques to further advance the field of electrochemical catalysis.

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The use of artificial intelligence in building engineering for historic buildings build in the Austro-Hungarian monarchy

Dvornik Perhavec,

Kamnik

2024

J. Comput. Cult. Herit.

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Knowledge discovery from databases (KDD) and data mining (DM) belong to the field of artificial intelligence (AI). The integration of artificial intelligence into various segments of the construction industry is still in its infancy, but it is expected to be used more widely soon, driven by the development of databases and data warehouses. By using BIM (Building Information Modelling) technologies in the planning of new buildings, we will be able to obtain valuable data. The situation is different for old, existing buildings and the building engineering associated with these properties. Civil engineers, renovation planners and architects need knowledge of the building before renovation. This knowledge is much less than the possibilities that exist. Information about the building can be found in provincial archives. For historic buildings, 10-15% of the plans, drawings, descriptions, or projects are available. The remaining 85% must be researched on site, which is a lengthy and costly process and hinders the construction process. The question arose as to how the findings from the study of buildings based on written and preserved sources can be applied to the 85% of buildings for which no data is available. This paper presents the use of the collected data as an idea for an initiative to develop a database and modelling using artificial intelligence algorithms. The research study investigates the feature "load-bearing wall" for residential buildings with basements and floors built between 1857 and 1948 in the former Austro-Hungarian Empire. The aim of this study is to create a model to predict the characteristics of a building for which no archival material is available. The study is based on the use of artificial intelligence in the creation of decision trees to help engineers improve their knowledge of historic buildings in the former Austro-Hungarian Empire and building engineering for historic objects.

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Synchrotron radiation data-driven artificial intelligence approaches in materials discovery

Cited by 3 publications

References 70 publications

Multidisciplinary and multiscale nanoscience research roadmap based on large scientific facilities

Multidisciplinary and multiscale nanoscience research roadmap based on large scientific facilities

In situ/Operando Investigation for Heterogeneous Electro-Catalysts: From Model Catalysts to State-of-the-Art Catalysts

The use of artificial intelligence in building engineering for historic buildings build in the Austro-Hungarian monarchy

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