Marcus M. Noack

605 total citations
25 papers, 298 citations indexed

About

Marcus M. Noack is a scholar working on Materials Chemistry, Artificial Intelligence and Electrical and Electronic Engineering. According to data from OpenAlex, Marcus M. Noack has authored 25 papers receiving a total of 298 indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Materials Chemistry, 7 papers in Artificial Intelligence and 6 papers in Electrical and Electronic Engineering. Recurrent topics in Marcus M. Noack's work include Machine Learning in Materials Science (7 papers), Gaussian Processes and Bayesian Inference (6 papers) and Advanced Multi-Objective Optimization Algorithms (4 papers). Marcus M. Noack is often cited by papers focused on Machine Learning in Materials Science (7 papers), Gaussian Processes and Bayesian Inference (6 papers) and Advanced Multi-Objective Optimization Algorithms (4 papers). Marcus M. Noack collaborates with scholars based in United States, Norway and Germany. Marcus M. Noack's 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 and has published in prestigious journals such as SHILAP Revista de lepidopterología, Advanced Energy Materials and Scientific Reports.

In The Last Decade

Marcus M. Noack

25 papers receiving 293 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Marcus M. Noack United States 10 141 53 47 35 30 25 298
Kevin Decker United States 5 259 1.8× 64 1.2× 37 0.8× 16 0.5× 88 2.9× 11 363
Luca Torresi Germany 4 299 2.1× 94 1.8× 64 1.4× 11 0.3× 51 1.7× 5 481
Spyros Zoupanos France 7 232 1.6× 76 1.4× 64 1.4× 6 0.2× 33 1.1× 12 408
Lingyun Yang China 12 104 0.7× 164 3.1× 28 0.6× 13 0.4× 22 0.7× 39 432
Maxwell Hutchinson United States 7 308 2.2× 76 1.4× 40 0.9× 9 0.3× 36 1.2× 9 483
Amalie Trewartha United States 9 419 3.0× 80 1.5× 204 4.3× 18 0.5× 42 1.4× 16 637
Tsuyoshi Ueno Japan 6 187 1.3× 65 1.2× 70 1.5× 13 0.4× 24 0.8× 9 331
Fatih Bulut Türkiye 17 82 0.6× 217 4.1× 12 0.3× 27 0.8× 28 0.9× 33 748
Henrik Schopmans Germany 4 300 2.1× 75 1.4× 61 1.3× 9 0.3× 43 1.4× 7 455
Johan Björck United States 7 92 0.7× 37 0.7× 162 3.4× 5 0.1× 37 1.2× 11 419

Countries citing papers authored by Marcus M. Noack

Since Specialization
Citations

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

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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-authorship network of co-authors of Marcus M. Noack

This figure shows the co-authorship network connecting the top 25 collaborators of Marcus M. Noack. A scholar is included among the top collaborators of Marcus M. Noack 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 Marcus M. Noack. Marcus M. Noack is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Ribet, Stephanie M., Marcus M. Noack, Georgios Varnavides, et al.. (2025). BEACON—automated aberration correction for scanning transmission electron microscopy using Bayesian optimization. npj Computational Materials. 11(1). 1 indexed citations
2.
Halder, Ansuman, Tung V. N. Nguyen, Do Kyung Lee, et al.. (2025). AI‐Driven Robot Enables Synthesis‐Property Relation Prediction for Metal Halide Perovskites in Humid Atmosphere. Advanced Energy Materials. 15(34). 3 indexed citations
3.
Collins-Wildman, Daniel L., et al.. (2025). Machine-learning-based efficient parameter space exploration for energy storage systems. Cell Reports Physical Science. 6(4). 102543–102543. 4 indexed citations
4.
Noack, Marcus M., et al.. (2024). A unifying perspective on non-stationary kernels for deeper Gaussian processes. SHILAP Revista de lepidopterología. 2(1). 7 indexed citations
5.
Noack, Marcus M., Harinarayan Krishnan, Mark D. Risser, & Kristofer G. Reyes. (2023). Exact Gaussian processes for massive datasets via non-stationary sparsity-discovering kernels. Scientific Reports. 13(1). 3155–3155. 11 indexed citations
6.
Noack, Marcus M. & Kristofer G. Reyes. (2023). Mathematical nuances of Gaussian process-driven autonomous experimentation. MRS Bulletin. 48(2). 153–163. 7 indexed citations
7.
Noack, Marcus M., et al.. (2023). Surface enrichment dictates block copolymer orientation. Nanoscale. 15(15). 6901–6912. 4 indexed citations
8.
Yager, Kevin G., et al.. (2023). Autonomous x-ray scattering. Nanotechnology. 34(32). 322001–322001. 17 indexed citations
9.
Doerk, Gregory S., et al.. (2023). Autonomous discovery of emergent morphologies in directed self-assembly of block copolymer blends. Science Advances. 9(2). eadd3687–eadd3687. 23 indexed citations
10.
Zhao, Chonghang, Marcus M. Noack, Jiun-Han Chen, et al.. (2022). Machine-learning for designing nanoarchitectured materials by dealloying. Communications Materials. 3(1). 9 indexed citations
11.
Noack, Marcus M. & James A. Sethian. (2022). Advanced stationary and nonstationary kernel designs for domain-aware Gaussian processes. arXiv (Cornell University). 17(1). 131–156. 20 indexed citations
12.
Noack, Marcus M., et al.. (2021). High-Performance Hybrid-Global-Deflated-Local Optimization with Applications to Active Learning. PubMed. 49. 24–29. 2 indexed citations
13.
Noack, Marcus M., et al.. (2020). Data Science and Machine Learning for polymer films and beyond. Bulletin of the American Physical Society. 1 indexed citations
14.
Noack, Marcus M., Gregory S. Doerk, Ruipeng Li, Masafumi Fukuto, & Kevin G. Yager. (2020). Advances in Kriging-Based Autonomous X-Ray Scattering Experiments. Scientific Reports. 10(1). 1325–1325. 30 indexed citations
15.
Noack, Marcus M., Taisuke Ohta, Thomas E. Beechem, et al.. (2020). K-means-driven Gaussian Process data collection for angle-resolved photoemission spectroscopy. Machine Learning Science and Technology. 1(4). 45015–45015. 12 indexed citations
16.
Noack, Marcus M., Kevin G. Yager, Masafumi Fukuto, et al.. (2019). A Kriging-Based Approach to Autonomous Experimentation with Applications to X-Ray Scattering. Scientific Reports. 9(1). 11809–11809. 73 indexed citations
17.
Noack, Marcus M. & Petrus H. Zwart. (2019). Computational Strategies to Increase Efficiency of Gaussian-Process-Driven Autonomous Experiments. 1–7. 1 indexed citations
18.
Noack, Marcus M. & Stuart Clark. (2017). Acoustic wave and eikonal equations in a transformed metric space for various types of anisotropy. Heliyon. 3(3). e00260–e00260. 6 indexed citations
19.
Noack, Marcus M., et al.. (2015). Fast computation of eikonal and transport equations on graphics processing units computer architectures. Geophysics. 80(5). T183–T9. 3 indexed citations
20.

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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