Daniel Höche

5.2k total citations
120 papers, 4.1k citations indexed

About

Daniel Höche is a scholar working on Materials Chemistry, Biomaterials and Mechanical Engineering. According to data from OpenAlex, Daniel Höche has authored 120 papers receiving a total of 4.1k indexed citations (citations by other indexed papers that have themselves been cited), including 95 papers in Materials Chemistry, 56 papers in Biomaterials and 37 papers in Mechanical Engineering. Recurrent topics in Daniel Höche's work include Magnesium Alloys: Properties and Applications (56 papers), Corrosion Behavior and Inhibition (45 papers) and Hydrogen Storage and Materials (21 papers). Daniel Höche is often cited by papers focused on Magnesium Alloys: Properties and Applications (56 papers), Corrosion Behavior and Inhibition (45 papers) and Hydrogen Storage and Materials (21 papers). Daniel Höche collaborates with scholars based in Germany, China and Portugal. Daniel Höche's co-authors include Mikhail L. Zheludkevich, Sviatlana V. Lamaka, Darya Snihirova, Min Deng, Bahram Vaghefinazari, Linqian Wang, Carsten Blawert, Peter Schaaf, Norbert Hort and Di Mei and has published in prestigious journals such as Journal of the American Chemical Society, SHILAP Revista de lepidopterología and Journal of Applied Physics.

In The Last Decade

Daniel Höche

114 papers receiving 4.0k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Daniel Höche Germany 34 3.0k 2.2k 1.3k 687 480 120 4.1k
Liang Wu China 43 5.0k 1.7× 3.8k 1.7× 2.1k 1.6× 600 0.9× 772 1.6× 214 6.6k
M. Aliofkhazraei Iran 28 2.9k 1.0× 1.1k 0.5× 1.1k 0.9× 790 1.1× 771 1.6× 69 4.2k
M. Bobby Kannan Australia 40 3.1k 1.1× 3.0k 1.4× 2.2k 1.7× 538 0.8× 343 0.7× 115 4.9k
Rajan Ambat Denmark 31 2.2k 0.7× 938 0.4× 1.7k 1.3× 1.5k 2.2× 435 0.9× 210 4.3k
Yawei Shao China 36 3.1k 1.1× 974 0.5× 1.3k 1.0× 406 0.6× 487 1.0× 122 4.3k
Yan Feng China 36 2.2k 0.7× 1.8k 0.8× 2.0k 1.5× 467 0.7× 401 0.8× 179 3.7k
Fen Zhang China 31 2.8k 0.9× 2.1k 1.0× 959 0.7× 564 0.8× 258 0.5× 142 3.8k
Xin Wang China 36 2.2k 0.7× 1.0k 0.5× 2.8k 2.1× 815 1.2× 751 1.6× 239 4.7k
M. Curioni United Kingdom 35 3.0k 1.0× 680 0.3× 900 0.7× 428 0.6× 286 0.6× 122 3.7k
S. Thomas Australia 34 3.3k 1.1× 2.3k 1.1× 3.7k 2.8× 312 0.5× 473 1.0× 72 6.1k

Countries citing papers authored by Daniel Höche

Since Specialization
Citations

This map shows the geographic impact of Daniel Höche'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 Daniel Höche with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Daniel Höche more than expected).

Fields of papers citing papers by Daniel Höche

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Daniel Höche. 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 Daniel Höche. The network helps show where Daniel Höche may publish in the future.

Co-authorship network of co-authors of Daniel Höche

This figure shows the co-authorship network connecting the top 25 collaborators of Daniel Höche. A scholar is included among the top collaborators of Daniel Höche 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 Daniel Höche. Daniel Höche 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.
Eiken, Janin, et al.. (2026). Machine learning-accelerated CALPHAD analysis of impurity-driven intermetallic formation in secondary AlSi7Mg0.3. SHILAP Revista de lepidopterología. 4(1).
2.
Wu, Yulong, Darya Snihirova, Tim Würger, et al.. (2025). Machine learning-guided discovery of high-efficiency electrolyte additives for aqueous magnesium-air batteries. Energy storage materials. 76. 104120–104120. 9 indexed citations
3.
Truong, Tam T., et al.. (2025). A hybrid deep learning model for predicting atmospheric corrosion in steel energy structures under maritime conditions based on time-series data. Results in Engineering. 25. 104417–104417. 3 indexed citations
4.
Arya, Vivek, et al.. (2024). Evaluation of innovative microreactor for examination of alkoxide pitting corrosion and data generation for numerical transient model. Materialwissenschaft und Werkstofftechnik. 55(3). 302–313. 1 indexed citations
5.
Würger, Tim, et al.. (2024). Influence of Simple Salts on Solvent Reduction Stability at Mg‐Alloy Anodes Interface: A Potential‐Dependent DFT Study. Advanced Energy Materials. 14(42). 1 indexed citations
6.
Zhang, Yue, Wen Xu, Cheng Wang, et al.. (2024). Localized accelerated degradation of magnesium: A new insight into the mechanism of its biomedical degradation. Corrosion Science. 237. 112335–112335. 10 indexed citations
7.
Blawert, Carsten, et al.. (2024). Effects of mechanical surface pre-treatment on integrity and corrosion of bare and coated AA6082 substrates. Journal of Materials Research and Technology. 31. 844–859. 2 indexed citations
8.
Höche, Daniel, et al.. (2024). Mastering the complex time-scale interaction during Stress Corrosion Cracking phenomena through an advanced coupling scheme. Computer Methods in Applied Mechanics and Engineering. 428. 117101–117101. 2 indexed citations
9.
Oechsner, Matthias, et al.. (2024). Mechanistic insights into chemical corrosion of AA1050 in ethanol‐blended fuels with water contamination via phase field modeling. Materials and Corrosion. 75(9). 1216–1227. 1 indexed citations
10.
Höche, Daniel, et al.. (2023). Effect of climatic parameters on marine atmospheric corrosion: correlation analysis of on-site sensors data. npj Materials Degradation. 7(1). 12 indexed citations
11.
12.
Seghier, Mohamed El Amine Ben, et al.. (2023). An intelligent framework for forecasting and investigating corrosion in marine conditions using time sensor data. npj Materials Degradation. 7(1). 12 indexed citations
13.
Höche, Daniel, et al.. (2023). Integrating Explainability into Graph Neural Network Models for the Prediction of X-ray Absorption Spectra. Journal of the American Chemical Society. 145(41). 22584–22598. 31 indexed citations
14.
Wiese, Björn, et al.. (2023). Property design of extruded magnesium-gadolinium alloys through machine learning. Materials Today Communications. 36. 106566–106566. 4 indexed citations
15.
Snihirova, Darya, Min Deng, Bahram Vaghefinazari, et al.. (2022). Sustainable aqueous metal-air batteries: An insight into electrolyte system. Energy storage materials. 52. 573–597. 92 indexed citations
16.
Seghier, Mohamed El Amine Ben, Daniel Höche, & Mikhail L. Zheludkevich. (2022). Prediction of the internal corrosion rate for oil and gas pipeline: Implementation of ensemble learning techniques. Journal of Natural Gas Science and Engineering. 99. 104425–104425. 106 indexed citations
17.
Würger, Tim, Linqian Wang, Darya Snihirova, et al.. (2022). Data-driven selection of electrolyte additives for aqueous magnesium batteries. Journal of Materials Chemistry A. 10(40). 21672–21682. 14 indexed citations
18.
Mir, Zahid, A.C. Bastos, Frederico Maia, et al.. (2021). The Stability and Chloride Entrapping Capacity of ZnAl-NO2 LDH in High-Alkaline/Cementitious Environment. MACAU (Kiel University). 2(1). 78–99. 11 indexed citations
19.
Mir, Zahid, et al.. (2019). Enhanced Predictive Modelling of Steel Corrosion in Concrete in Submerged Zone Based on a Dynamic Activation Approach. International Journal of Concrete Structures and Materials. 13(1). 16 indexed citations
20.
Schaaf, Peter, et al.. (2009). Transformation of expanded austenite to an amorphous ferromagnetic surface layer during laser carburization of austenitic stainless steel. HTM Journal of Heat Treatment and Materials. 64(4). 242–248.

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.

Explore authors with similar magnitude of impact

Breakdown of academic impact, for papers by Liang Wu Breakdown of academic impact, for papers by M. Aliofkhazraei Breakdown of academic impact, for papers by M. Bobby Kannan Breakdown of academic impact, for papers by Rajan Ambat Breakdown of academic impact, for papers by Yawei Shao Breakdown of academic impact, for papers by Yan Feng Breakdown of academic impact, for papers by Fen Zhang Breakdown of academic impact, for papers by Xin Wang Breakdown of academic impact, for papers by M. Curioni Breakdown of academic impact, for papers by S. Thomas
Rankless by CCL
2026