Diego Prada‐Gracia
- Molecular Biology
- Atomic and Molecular Physics, and Optics
- Materials Chemistry
- Computational Theory and Mathematics top 5%
- Cellular and Molecular Neuroscience
- Co-authors
- P. M. EcheniqueF. FaloJesús Gómez‐GardeñesSara Huerta‐YépezFrancesco RaoDavide ProvasiMarta FilizolaKristen A. Marino
- Topics
- Protein Structure and Dynamics (8 papers)Spectroscopy and Quantum Chemical Studies (7 papers)Computational Drug Discovery Methods (3 papers)
- Cited by
- Computational Theory and MathematicsMolecular BiologyAtomic and Molecular Physics, and Optics
- Partner nations
- MexicoGermanyUnited States
In The Last Decade
Diego Prada‐Gracia
25 papers receiving 588 citations
Peers
Comparison fields: 5 of 108
- Molecular Biology 350
- Atomic and Molecular Physics, and Optics 147
- Materials Chemistry 117
- Computational Theory and Mathematics 83
- Cellular and Molecular Neuroscience 60
Countries citing papers authored by Diego Prada‐Gracia
This map shows the geographic impact of Diego Prada‐Gracia'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 Diego Prada‐Gracia with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Diego Prada‐Gracia more than expected).
Fields of papers citing papers by Diego Prada‐Gracia
This network shows the impact of papers produced by Diego Prada‐Gracia. 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 Diego Prada‐Gracia. The network helps show where Diego Prada‐Gracia may publish in the future.
Co-authorship network of co-authors of Diego Prada‐Gracia
This figure shows the co-authorship network connecting the top 25 collaborators of Diego Prada‐Gracia. A scholar is included among the top collaborators of Diego Prada‐Gracia 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 Diego Prada‐Gracia. Diego Prada‐Gracia is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 7 | |
| 2 | 10 | |
| 3 | 1 | |
| 4 | 6 | |
| 5 | 4 | |
| 6 | 17 | |
| 7 | 4 | |
| 8 | 13 | |
| 9 | 14 | |
| 10 | 12 | |
| 11 | 39 | |
| 12 | 48 | |
| 13 | 61 | |
| 14 | 0 | |
| 15 | 4 | |
| 16 | 16 | |
| 17 | 12 | |
| 18 | 132 | |
| 19 | 90 | |
| 20 | 24 |
About Diego Prada‐Gracia
Diego Prada‐Gracia is a scholar working on Acoustics and Ultrasonics, Microbiology and Molecular Biology, having authored 26 papers that have together received 606 indexed citations. Recurring topics across this work include Protein Structure and Dynamics (8 papers), Spectroscopy and Quantum Chemical Studies (7 papers) and Computational Drug Discovery Methods (3 papers). The work is most often cited by research in Computational Theory and Mathematics (83 citations), Molecular Biology (350 citations) and Atomic and Molecular Physics, and Optics (147 citations). Diego Prada‐Gracia has collaborated with scholars based in Mexico, Germany and United States. Frequent co-authors include P. M. Echenique, F. Falo, Jesús Gómez‐Gardeñes, Sara Huerta‐Yépez, Francesco Rao, Davide Provasi, Marta Filizola, Kristen A. Marino, José L. Alonso and Fernando Falceto. Their work appears in journals such as Physical Review Letters, Nature Communications and The Journal of Chemical Physics.
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.