Anthony Wang
Impact in
- Materials Chemistry top 10%
- Machine Learning in Materials Science
- X-ray Diffraction in Crystallography
- Electronic and Structural Properties of Oxides
- Advanced Thermoelectric Materials and Devices
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- Computational Drug Discovery Methods
Papers in
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- Machine Learning in Materials Science 4
- X-ray Diffraction in Crystallography 1
- Phase-change materials and chalcogenides 1
- Quantum Dots Synthesis And Properties 1
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- Computational Drug Discovery Methods 4
- Co-authors
- Steven K. Kauwe (3 shared papers)Taylor D. Sparks (3 shared papers)Ryan Murdock (3 shared papers)Aleksander Gurlo (2 shared papers)Anton O. Oliynyk (1 shared paper)Kristin A. Persson (1 shared paper)Jakoah Brgoch (1 shared paper)Antonio Massimiliano Mio (1 shared paper)
- Journals
- Integrating materials and manufacturing innovation (2 papers)npj Computational Materials (1 paper)Chemistry of Materials (1 paper)Journal of Physics Condensed Matter (1 paper)
- Partner nations
- GermanyUnited States
In The Last Decade
Anthony Wang
5 papers receiving 590 citations
Anthony Wang's Hit Papers
Peers
Comparison fields: 5 of 72
- Materials Chemistry 458
- Computational Theory and Mathematics 100
- Metals and Alloys 14
- Catalysis 37
- Mechanical Engineering 85
Countries citing papers authored by Anthony Wang
This map shows the geographic impact of Anthony Wang's research. 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 Anthony Wang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Anthony Wang more than expected).
Fields of papers citing papers by Anthony Wang
This network shows the impact of papers produced by Anthony Wang. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Anthony Wang. The network helps show where Anthony Wang may publish in the future.
Co-authors
The 10 scholars most cited alongside Anthony Wang, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | Machine Learning for Materials Scientists: An Introductory Guide toward Best Practices Hit paper breakdown → | 2020 | 346 |
| 2 | 2021 | 151 | |
| 3 | 2020 | 66 | |
| 4 | 2022 | 19 | |
| 5 | 2019 | 16 |
About Anthony Wang
Anthony Wang is a scholar working on Materials Chemistry, Computational Theory and Mathematics, Artificial Intelligence, Catalysis and Electrical and Electronic Engineering, having authored 5 papers that have together received 598 indexed citations. Recurring topics across this work include Computational Drug Discovery Methods (4 papers), Machine Learning in Materials Science (4 papers), Chalcogenide Semiconductor Thin Films (1 paper), X-ray Diffraction in Crystallography (1 paper), Advanced Graph Neural Networks (1 paper), Catalysis and Oxidation Reactions (1 paper), Phase-change materials and chalcogenides (1 paper) and Quantum Dots Synthesis And Properties (1 paper). The work is most often cited by research in Materials Chemistry (458 citations), Computational Theory and Mathematics (100 citations), Metals and Alloys (14 citations), Catalysis (37 citations) and Mechanical Engineering (85 citations). Anthony Wang has collaborated with scholars based in Germany and United States. Frequent co-authors include Steven K. Kauwe, Taylor D. Sparks, Ryan Murdock, Aleksander Gurlo, Anton O. Oliynyk, Kristin A. Persson, Jakoah Brgoch, Antonio Massimiliano Mio, Matthias Wuttig and Oana Cojocaru‐Mirédin. Their work appears in journals such as Integrating materials and manufacturing innovation, npj Computational Materials, Chemistry of Materials and Journal of Physics Condensed Matter.
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.