Jarosław M. Granda
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- Computational Drug Discovery Methods 3
- Materials Chemistry top 5%
- Machine Learning in Materials Science 4
- Luminescence and Fluorescent Materials 4
- Biomedical Engineering top 5%
- Innovative Microfluidic and Catalytic Techniques Innovation 6
- Spectroscopy top 5%
- Molecular Sensors and Ion Detection 8
- Analytical Chemistry and Chromatography 6
- Catalysis top 10%
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- Chemical Synthesis and Analysis 2
- DNA and Biological Computing 2
- Co-authors
- Leroy CroninVincenza DragoneDe‐Liang LongPiotr S. GromskiAlon HensonJakob B. WolfDavide AngeloneJanusz Jurczak
- Partner nations
- PolandUnited KingdomUnited States
In The Last Decade
Jarosław M. Granda
21 papers receiving 1.6k citations
Hit Papers
Peers
Comparison fields: 5 of 117
- Computational Theory and Mathematics 378
- Materials Chemistry 850
- Biomedical Engineering 552
- Spectroscopy 171
- Catalysis 71
Countries citing papers authored by Jarosław M. Granda
This map shows the geographic impact of Jarosław M. Granda'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 Jarosław M. Granda with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jarosław M. Granda more than expected).
Fields of papers citing papers by Jarosław M. Granda
This network shows the impact of papers produced by Jarosław M. Granda. 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 Jarosław M. Granda. The network helps show where Jarosław M. Granda may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Jarosław M. Granda, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2025 | 0 | |
| 2 | 2024 | 10 | |
| 3 | 2024 | 7 | |
| 4 | 2023 | 25 | |
| 5 | 2021 | 53 | |
| 6 | 2020 | 67 | |
| 7 | 2019 | 98 | |
| 8 | 2019 | 174 | |
| 9 | Organic synthesis in a modular robotic system driven by a chemical programming languagebreakdown → | 2018 | 438 |
| 10 | 2018 | 11 | |
| 11 | Controlling an organic synthesis robot with machine learning to search for new reactivitybreakdown → | 2018 | 523 |
| 12 | 2018 | 22 | |
| 13 | 2017 | 58 | |
| 14 | 2015 | 17 | |
| 15 | 2015 | 20 | |
| 16 | 2015 | 11 | |
| 17 | 2014 | 8 | |
| 18 | 2014 | 9 | |
| 19 | 2013 | 24 | |
| 20 | 2013 | 17 |
About Jarosław M. Granda
Jarosław M. Granda is a scholar working on Spectroscopy, Physical and Theoretical Chemistry and Organic Chemistry, having authored 23 papers that have together received 1.6k indexed citations. Recurring topics across this work include Molecular Sensors and Ion Detection (8 papers), Innovative Microfluidic and Catalytic Techniques Innovation (6 papers), Analytical Chemistry and Chromatography (6 papers), Machine Learning in Materials Science (4 papers), Luminescence and Fluorescent Materials (4 papers), Computational Drug Discovery Methods (3 papers), Chemical Synthesis and Analysis (2 papers) and DNA and Biological Computing (2 papers). The work is most often cited by research in Computational Theory and Mathematics (378 citations), Materials Chemistry (850 citations) and Biomedical Engineering (552 citations). Jarosław M. Granda has collaborated with scholars based in Poland, United Kingdom and United States. Frequent co-authors include Leroy Cronin, Vincenza Dragone, De‐Liang Long, Piotr S. Gromski, Alon Henson, Jakob B. Wolf, Davide Angelone, Janusz Jurczak, Gerardo Aragón-Camarasa and Sebastian Steiner. Their work appears in journals such as Nature, Science and Nature Communications.
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.