Jake Graser

762 total citations
8 papers, 600 citations indexed

About

Jake Graser is a scholar working on Materials Chemistry, Computational Theory and Mathematics and Electrical and Electronic Engineering. According to data from OpenAlex, Jake Graser has authored 8 papers receiving a total of 600 indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Materials Chemistry, 2 papers in Computational Theory and Mathematics and 2 papers in Electrical and Electronic Engineering. Recurrent topics in Jake Graser's work include Machine Learning in Materials Science (4 papers), X-ray Diffraction in Crystallography (3 papers) and Computational Drug Discovery Methods (2 papers). Jake Graser is often cited by papers focused on Machine Learning in Materials Science (4 papers), X-ray Diffraction in Crystallography (3 papers) and Computational Drug Discovery Methods (2 papers). Jake Graser collaborates with scholars based in United States, Jordan and Brazil. Jake Graser's co-authors include Taylor D. Sparks, Steven K. Kauwe, Ryan Murdock, Michael J. O’Connell, Fei Gao, Yuan Wang, Chengwei Wang, Ran Zhao, Clayton Cozzan and Guillaume Lheureux and has published in prestigious journals such as ACS Nano, Chemistry of Materials and ACS Applied Materials & Interfaces.

In The Last Decade

Jake Graser

7 papers receiving 585 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Jake Graser United States 7 428 195 92 81 68 8 600
Zeyu Liu United States 13 513 1.2× 161 0.8× 67 0.7× 77 1.0× 32 0.5× 24 669
Christopher K. H. Borg United States 12 612 1.4× 258 1.3× 204 2.2× 122 1.5× 39 0.6× 17 843
Aria Mansouri Tehrani United States 14 1.1k 2.5× 359 1.8× 118 1.3× 154 1.9× 142 2.1× 27 1.2k
Erin Antono United States 12 613 1.4× 390 2.0× 89 1.0× 128 1.6× 144 2.1× 15 987
Masayoshi Yamazaki Japan 11 576 1.3× 127 0.7× 59 0.6× 390 4.8× 75 1.1× 47 901
Kazuki Shitara Japan 17 883 2.1× 357 1.8× 123 1.3× 189 2.3× 76 1.1× 40 1.1k
Chuhong Wang United States 11 411 1.0× 315 1.6× 23 0.3× 91 1.1× 62 0.9× 26 738
Masaya Kumagai Japan 12 249 0.6× 265 1.4× 285 3.1× 69 0.9× 24 0.4× 50 633

Countries citing papers authored by Jake Graser

Since Specialization
Citations

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

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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-authorship network of co-authors of Jake Graser

This figure shows the co-authorship network connecting the top 25 collaborators of Jake Graser. A scholar is included among the top collaborators of Jake Graser based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Jake Graser. Jake Graser is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

8 of 8 papers shown
1.
Graser, Jake, et al.. (2024). Thermoelectric properties of TaVO5 and GdTaO4: An experimental verification of machine learning prediction. Advances in Applied Ceramics Structural Functional and Bioceramics. 123(1-3). 15–21.
2.
Kauwe, Steven K., Jake Graser, Ryan Murdock, & Taylor D. Sparks. (2019). Can machine learning find extraordinary materials?. Computational Materials Science. 174. 109498–109498. 80 indexed citations
3.
Nelson, Isaac, et al.. (2019). Freeze‐Casting of Surface‐Magnetized Iron(II,III) Oxide Particles in a Uniform Static Magnetic Field Generated by a Helmholtz Coil. Advanced Engineering Materials. 21(3). 37 indexed citations
4.
Graser, Jake, Steven K. Kauwe, & Taylor D. Sparks. (2018). Machine Learning and Energy Minimization Approaches for Crystal Structure Predictions: A Review and New Horizons. Chemistry of Materials. 30(11). 3601–3612. 159 indexed citations
5.
Cozzan, Clayton, Guillaume Lheureux, Emily E. Levin, et al.. (2018). Stable, Heat-Conducting Phosphor Composites for High-Power Laser Lighting. ACS Applied Materials & Interfaces. 10(6). 5673–5681. 135 indexed citations
6.
7.
Kauwe, Steven K., et al.. (2018). Machine Learning Prediction of Heat Capacity for Solid Inorganics. Integrating materials and manufacturing innovation. 7(2). 43–51. 77 indexed citations
8.
Wang, Chengwei, Yuan Wang, Jake Graser, et al.. (2013). Solution-Based Carbohydrate Synthesis of Individual Solid, Hollow, and Porous Carbon Nanospheres Using Spray Pyrolysis. ACS Nano. 7(12). 11156–11165. 94 indexed citations

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.

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2026