Marcus M. Noack
Impact in
- Structural Biology top 10%
-
- Scientific Computing and Data Management
Papers in
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- Machine Learning in Materials Science 7
- Block Copolymer Self-Assembly 2
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- Gaussian Processes and Bayesian Inference 6
- Co-authors
- Kevin G. Yager (7 shared papers)Masafumi Fukuto (6 shared papers)Gregory S. Doerk (4 shared papers)James A. Sethian (3 shared papers)Ruipeng Li (2 shared papers)Stephen J. Harris (2 shared papers)Kristofer G. Reyes (2 shared papers)Mark D. Risser (2 shared papers)
- Journals
- Scientific Reports (3 papers)npj Computational Materials (2 papers)Science Advances (1 paper)MRS Bulletin (1 paper)Machine Learning Science and Technology (1 paper)
- Partner nations
- United StatesNorwayGermany
In The Last Decade
Marcus M. Noack
25 papers receiving 293 citations
Peers
Comparison fields: 5 of 76
- Structural Biology 17
- Information Systems and Management 27
- Materials Chemistry 141
- Automotive Engineering 35
- Surfaces, Coatings and Films 20
Countries citing papers authored by Marcus M. Noack
This map shows the geographic impact of Marcus M. Noack'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 Marcus M. Noack with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Marcus M. Noack more than expected).
Fields of papers citing papers by Marcus M. Noack
This network shows the impact of papers produced by Marcus M. Noack. 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 Marcus M. Noack. The network helps show where Marcus M. Noack may publish in the future.
Co-authors
The 25 scholars most cited alongside Marcus M. Noack, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 25 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2019 | 73 | |
| 2 | 2020 | 30 | |
| 3 | 2023 | 23 | |
| 4 | 2019 | 23 | |
| 5 | 2022 | 20 | |
| 6 | 2023 | 17 | |
| 7 | 2022 | 17 | |
| 8 | 2023 | 13 | |
| 9 | 2020 | 12 | |
| 10 | 2023 | 11 | |
| 11 | 2022 | 9 | |
| 12 | 2024 | 7 | |
| 13 | 2023 | 7 | |
| 14 | 2023 | 7 | |
| 15 | 2017 | 6 | |
| 16 | 2025 | 4 | |
| 17 | 2023 | 4 | |
| 18 | 2025 | 3 | |
| 19 | 2015 | 3 | |
| 20 | 2022 | 3 |
About Marcus M. Noack
Marcus M. Noack is a scholar working on Materials Chemistry, Artificial Intelligence, Electrical and Electronic Engineering, Computational Theory and Mathematics and Automotive Engineering, having authored 25 papers that have together received 298 indexed citations. Recurring topics across this work include Machine Learning in Materials Science (7 papers), Gaussian Processes and Bayesian Inference (6 papers), Advanced Multi-Objective Optimization Algorithms (4 papers), Electron and X-Ray Spectroscopy Techniques (2 papers), Block Copolymer Self-Assembly (2 papers), Seismic Waves and Analysis (2 papers), Advanced Battery Technologies Research (2 papers) and Advancements in Photolithography Techniques (2 papers). The work is most often cited by research in Structural Biology (17 citations), Information Systems and Management (27 citations), Materials Chemistry (141 citations), Automotive Engineering (35 citations) and Surfaces, Coatings and Films (20 citations). Marcus M. Noack has collaborated with scholars based in United States, Norway and Germany. Frequent co-authors include Kevin G. Yager, Masafumi Fukuto, Gregory S. Doerk, James A. Sethian, Ruipeng Li, Stephen J. Harris, Kristofer G. Reyes, Mark D. Risser, Aaron Stein and Harinarayan Krishnan. Their work appears in journals such as Scientific Reports, npj Computational Materials, Science Advances, MRS Bulletin and Machine Learning Science and Technology.
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.