Tim Mueller
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- Electrocatalysts for Energy Conversion 14
- Catalysis top 1%
- Catalysis and Oxidation Reactions 6
- Materials Chemistry top 1%
- Machine Learning in Materials Science 22
- X-ray Diffraction in Crystallography 8
- Electrochemistry top 1%
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- Advanced Battery Materials and Technologies 8
- Advancements in Battery Materials 7
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- Advanced materials and composites 6
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- nanoparticles nucleation surface interactions 6
- Co-authors
- Gerbrand CederLiang CaoAnubhav JainGeoffroy HautierChristopher C. FischerZipeng ZhaoXiangfeng DuanYu Huang
- Partner nations
- United StatesGermanyJapan
In The Last Decade
Tim Mueller
68 papers receiving 7.6k citations
Hit Papers
Peers
Comparison fields: 5 of 109
- Renewable Energy, Sustainability and the Environment 3.0k
- Catalysis 746
- Materials Chemistry 3.9k
- Electrochemistry 398
- Electrical and Electronic Engineering 3.1k
Countries citing papers authored by Tim Mueller
This map shows the geographic impact of Tim Mueller'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 Tim Mueller with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Tim Mueller more than expected).
Fields of papers citing papers by Tim Mueller
This network shows the impact of papers produced by Tim Mueller. 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 Tim Mueller. The network helps show where Tim Mueller may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Tim Mueller, 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 | 2023 | 2 | |
| 2 | 2023 | 24 | |
| 3 | 2023 | 18 | |
| 4 | 2022 | 22 | |
| 5 | 2022 | 73 | |
| 6 | Machine learning for alloysbreakdown → | 2021 | 478 |
| 7 | 2020 | 50 | |
| 8 | 2020 | 25 | |
| 9 | 2019 | 84 | |
| 10 | 2019 | 19 | |
| 11 | 2019 | 104 | |
| 12 | 2019 | 306 | |
| 13 | 2019 | 12 | |
| 14 | 2018 | 22 | |
| 15 | 2018 | 54 | |
| 16 | 2017 | 22 | |
| 17 | 2016 | 233 | |
| 18 | 2016 | 185 | |
| 19 | High-performance transition metal–doped Pt 3 Ni octahedra for oxygen reduction reactionbreakdown → | 2015 | 1711 |
| 20 | 1999 | 15 |
About Tim Mueller
Tim Mueller is a scholar working on Catalysis, Renewable Energy, Sustainability and the Environment, Materials Chemistry, Ceramics and Composites and Automotive Engineering, having authored 69 papers that have together received 7.8k indexed citations. Recurring topics across this work include Machine Learning in Materials Science (22 papers), Electrocatalysts for Energy Conversion (14 papers), X-ray Diffraction in Crystallography (8 papers), Advanced Battery Materials and Technologies (8 papers), Advancements in Battery Materials (7 papers), Catalysis and Oxidation Reactions (6 papers), Advanced materials and composites (6 papers) and nanoparticles nucleation surface interactions (6 papers). The work is most often cited by research in Renewable Energy, Sustainability and the Environment (3.0k citations), Catalysis (746 citations), Materials Chemistry (3.9k citations), Electrochemistry (398 citations) and Electrical and Electronic Engineering (3.1k citations). Tim Mueller has collaborated with scholars based in United States, Germany and Japan. Frequent co-authors include Gerbrand Ceder, Liang Cao, Anubhav Jain, Geoffroy Hautier, Christopher C. Fischer, Zipeng Zhao, Xiangfeng Duan, Yu Huang, Mufan Li and Chuhong Wang. Their work appears in journals such as Chemistry of Materials, Physical Review B, ACS Catalysis, Nano Letters and npj Computational Materials.
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.