Jake Graser
Impact in
- Materials Chemistry top 10%
- Machine Learning in Materials Science
- Luminescence Properties of Advanced Materials
- X-ray Diffraction in Crystallography
Papers in
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- Machine Learning in Materials Science 4
- X-ray Diffraction in Crystallography 3
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- Computational Drug Discovery Methods 2
- Co-authors
- Taylor D. Sparks (7 shared papers)Steven K. Kauwe (3 shared papers)Ryan Murdock (1 shared paper)Chengwei Wang (1 shared paper)Fei Gao (1 shared paper)Michael J. O’Connell (1 shared paper)Yuan Wang (1 shared paper)Ran Zhao (1 shared paper)
- Journals
- Advances in Applied Ceramics Structural Functional and Bioceramics (1 paper)Ceramics International (1 paper)Chemistry of Materials (1 paper)Advanced Engineering Materials (1 paper)Integrating materials and manufacturing innovation (1 paper)
- Partner nations
- United StatesBrazilUnited Kingdom
In The Last Decade
Jake Graser
7 papers receiving 585 citations
Peers
Comparison fields: 5 of 69
- Acoustics and Ultrasonics 9
- Materials Chemistry 428
- Ceramics and Composites 37
- Electronic, Optical and Magnetic Materials 92
- Computational Theory and Mathematics 68
Countries citing papers authored by Jake Graser
This map shows the geographic impact of Jake Graser'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 Jake Graser with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jake Graser more than expected).
Fields of papers citing papers by Jake Graser
This network shows the impact of papers produced by Jake Graser. 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 Jake Graser. The network helps show where Jake Graser may publish in the future.
Co-authors
The 19 scholars most cited alongside Jake Graser, 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 | 2018 | 159 | |
| 2 | 2018 | 135 | |
| 3 | 2013 | 94 | |
| 4 | 2019 | 80 | |
| 5 | 2018 | 77 | |
| 6 | 2019 | 37 | |
| 7 | 2018 | 18 | |
| 8 | 2024 | 0 |
About Jake Graser
Jake Graser is a scholar working on Materials Chemistry, Computational Theory and Mathematics, Electrical and Electronic Engineering, Mechanics of Materials and Ocean Engineering, having authored 8 papers that have together received 600 indexed citations. Recurring topics across this work include Machine Learning in Materials Science (4 papers), X-ray Diffraction in Crystallography (3 papers), Computational Drug Discovery Methods (2 papers), Calcium Carbonate Crystallization and Inhibition (1 paper), Gas Sensing Nanomaterials and Sensors (1 paper), Supercapacitor Materials and Fabrication (1 paper), Hydrocarbon exploration and reservoir analysis (1 paper) and Magnetic and Electromagnetic Effects (1 paper). The work is most often cited by research in Acoustics and Ultrasonics (9 citations), Materials Chemistry (428 citations), Ceramics and Composites (37 citations), Electronic, Optical and Magnetic Materials (92 citations) and Computational Theory and Mathematics (68 citations). Jake Graser has collaborated with scholars based in United States, Brazil and United Kingdom. Frequent co-authors include Taylor D. Sparks, Steven K. Kauwe, Ryan Murdock, Chengwei Wang, Fei Gao, Michael J. O’Connell, Yuan Wang, Ran Zhao, Shuji Nakamura and Claude Weisbuch. Their work appears in journals such as Advances in Applied Ceramics Structural Functional and Bioceramics, Ceramics International, Chemistry of Materials, Advanced Engineering Materials and Integrating materials and manufacturing innovation.
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.