Anthony Wang
- Materials Chemistry top 10%
- Machine Learning in Materials Science 4
- X-ray Diffraction in Crystallography 1
- Quantum Dots Synthesis And Properties 1
- Phase-change materials and chalcogenides 1
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- Computational Drug Discovery Methods 4
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- Chalcogenide Semiconductor Thin Films 1
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- Advanced Materials Characterization Techniques 1
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- Advanced Graph Neural Networks 1
- Co-authors
- Steven K. KauweRyan MurdockTaylor D. SparksAleksander GurloAnton O. OliynykJakoah BrgochKristin A. PerssonMatthias Wuttig
- Journals
- Integrating materials and manufacturing innovation (2 papers)npj Computational Materials (1 paper)Chemistry of Materials (1 paper)
- Partner nations
- GermanyUnited States
In The Last Decade
Anthony Wang
5 papers receiving 537 citations
Hit Papers
Peers
Comparison fields: 5 of 72
- Materials Chemistry 425
- Computational Theory and Mathematics 95
- Metals and Alloys 14
- Catalysis 36
- Health Informatics 3
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-authorship network
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 | 2022 | 19 | |
| 2 | 2021 | 140 | |
| 3 | Machine Learning for Materials Scientists: An Introductory Guide toward Best Practicesbreakdown → | 2020 | 323 |
| 4 | 2020 | 56 | |
| 5 | 2019 | 16 |
About Anthony Wang
Anthony Wang is a scholar working on Computational Theory and Mathematics, Catalysis, Materials Chemistry, Artificial Intelligence and Biomedical Engineering, having authored 5 papers that have together received 554 indexed citations. Recurring topics across this work include Machine Learning in Materials Science (4 papers), Computational Drug Discovery Methods (4 papers), X-ray Diffraction in Crystallography (1 paper), Quantum Dots Synthesis And Properties (1 paper), Chalcogenide Semiconductor Thin Films (1 paper), Advanced Materials Characterization Techniques (1 paper), Phase-change materials and chalcogenides (1 paper) and Advanced Graph Neural Networks (1 paper). The work is most often cited by research in Materials Chemistry (425 citations), Computational Theory and Mathematics (95 citations), Metals and Alloys (14 citations), Catalysis (36 citations) and Health Informatics (3 citations). Anthony Wang has collaborated with scholars based in Germany and United States. Frequent co-authors include Steven K. Kauwe, Ryan Murdock, Taylor D. Sparks, Aleksander Gurlo, Anton O. Oliynyk, Jakoah Brgoch, Kristin A. Persson, Matthias Wuttig, Antonio Massimiliano Mio 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.