Janosh Riebesell
About
In The Last Decade
Janosh Riebesell
7 papers receiving 535 citations
Hit Papers
Peers
Comparison fields: 5 of 52
- Materials Chemistry 442
- Electrical and Electronic Engineering 144
- Computational Theory and Mathematics 76
- Mechanical Engineering 43
- Biomedical Engineering 34
Countries citing papers authored by Janosh Riebesell
This map shows the geographic impact of Janosh Riebesell'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 Janosh Riebesell with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Janosh Riebesell more than expected).
Fields of papers citing papers by Janosh Riebesell
This network shows the impact of papers produced by Janosh Riebesell. 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 Janosh Riebesell. The network helps show where Janosh Riebesell may publish in the future.
Co-authorship network of co-authors of Janosh Riebesell
This figure shows the co-authorship network connecting the top 25 collaborators of Janosh Riebesell. A scholar is included among the top collaborators of Janosh Riebesell 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 Janosh Riebesell. Janosh Riebesell is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | A framework to evaluate machine learning crystal stability predictions breakdown → | 29 |
| 3 | Systematic softening in universal machine learning interatomic potentials breakdown → | 51 |
| 4 | 1 | |
| 5 | 2 | |
| 6 | 21 | |
| 7 | 4 | |
| 8 | CHGNet as a pretrained universal neural network potential for charge-informed atomistic modelling breakdown → | 444 |
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.