Rubén Laplaza
- Materials Chemistry top 10%
- Organic Chemistry top 10%
- Computational Theory and Mathematics top 5%
- Molecular Biology
- Inorganic Chemistry top 10%
- Co-authors
- Clémence CorminbœufJulia Contreras‐GarcíaFrancesca PeccatiRoberto A. BotoAlessandra CarboneChaoyu QuanYvon MadayJean‐Philip Piquemal
- Topics
- Machine Learning in Materials Science (22 papers)Computational Drug Discovery Methods (13 papers)Crystallography and molecular interactions (10 papers)
- Cited by
- Process Chemistry and TechnologyPhysical and Theoretical ChemistryComputational Theory and Mathematics
- Journals
- Journal of the American Chemical SocietyAdvanced MaterialsAngewandte Chemie International Edition
- Partner nations
- SwitzerlandSpainFrance
In The Last Decade
Rubén Laplaza
46 papers receiving 883 citations
Peers
Comparison fields: 5 of 87
- Materials Chemistry 410
- Organic Chemistry 305
- Computational Theory and Mathematics 188
- Molecular Biology 171
- Inorganic Chemistry 152
Countries citing papers authored by Rubén Laplaza
This map shows the geographic impact of Rubén Laplaza'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 Rubén Laplaza with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Rubén Laplaza more than expected).
Fields of papers citing papers by Rubén Laplaza
This network shows the impact of papers produced by Rubén Laplaza. 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 Rubén Laplaza. The network helps show where Rubén Laplaza may publish in the future.
Co-authorship network of co-authors of Rubén Laplaza
This figure shows the co-authorship network connecting the top 25 collaborators of Rubén Laplaza. A scholar is included among the top collaborators of Rubén Laplaza 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 Rubén Laplaza. Rubén Laplaza is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 10 | |
| 2 | 6 | |
| 3 | 4 | |
| 4 | 9 | |
| 5 | 4 | |
| 6 | 6 | |
| 7 | 5 | |
| 8 | 7 | |
| 9 | 4 | |
| 10 | 3 | |
| 11 | 5 | |
| 12 | 3 | |
| 13 | 8 | |
| 14 | 5 | |
| 15 | 61 | |
| 16 | 27 | |
| 17 | 16 | |
| 18 | 73 | |
| 19 | 9 | |
| 20 | 11 |
About Rubén Laplaza
Rubén Laplaza is a scholar working on Physical and Theoretical Chemistry, Process Chemistry and Technology and Computational Theory and Mathematics, having authored 46 papers that have together received 890 indexed citations. Recurring topics across this work include Machine Learning in Materials Science (22 papers), Computational Drug Discovery Methods (13 papers) and Crystallography and molecular interactions (10 papers). The work is most often cited by research in Process Chemistry and Technology (43 citations), Physical and Theoretical Chemistry (124 citations) and Computational Theory and Mathematics (188 citations). Rubén Laplaza has collaborated with scholars based in Switzerland, Spain and France. Frequent co-authors include Clémence Corminbœuf, Julia Contreras‐García, Francesca Peccati, Roberto A. Boto, Alessandra Carbone, Chaoyu Quan, Yvon Maday, Jean‐Philip Piquemal, Matthew D. Wodrich and Shubhajit Das. Their work appears in journals such as Journal of the American Chemical Society, Advanced Materials and Angewandte Chemie International Edition.
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.