Deep-Learning-Enabled On-Demand Design of Chiral Metamaterials

720 indexed citations

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This paper, published in 2018, received 720 indexed citations. Written by Wei Ma, Feng Cheng and Yongmin Liu covering the research area of Electronic, Optical and Magnetic Materials, Biomedical Engineering and Artificial Intelligence. It is primarily cited by scholars working on Electronic, Optical and Magnetic Materials (358 citations), Electrical and Electronic Engineering (277 citations) and Biomedical Engineering (248 citations). Published in ACS Nano.

Countries where authors are citing Deep-Learning-Enabled On-Demand Design of Chiral Metamaterials

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This map shows the geographic impact of Deep-Learning-Enabled On-Demand Design of Chiral Metamaterials. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Deep-Learning-Enabled On-Demand Design of Chiral Metamaterials with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Deep-Learning-Enabled On-Demand Design of Chiral Metamaterials more than expected).

Fields of papers citing Deep-Learning-Enabled On-Demand Design of Chiral Metamaterials

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Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of Deep-Learning-Enabled On-Demand Design of Chiral Metamaterials. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Deep-Learning-Enabled On-Demand Design of Chiral Metamaterials.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

This paper is also available at doi.org/10.1021/acsnano.8b03569.

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