Jon Paul Janet
- Computational Theory and Mathematics top 0.5%
- Computational Drug Discovery Methods 26
- Inorganic Chemistry top 2%
- Catalysis top 5%
- Materials Chemistry top 5%
- Machine Learning in Materials Science 30
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- Electrocatalysts for Energy Conversion 4
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- Protein Structure and Dynamics 14
- Metabolomics and Mass Spectrometry Studies 3
- Chemical Synthesis and Analysis 2
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- Innovative Microfluidic and Catalytic Techniques Innovation 4
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- Molecular Junctions and Nanostructures 2
- Co-authors
- Heather J. KulikAditya NandyChenru DuanTzuhsiung YangOla EngkvistYongjin LeeSeyed Mohamad MoosaviBerend Smit
- Partner nations
- SwedenUnited StatesUnited Kingdom
In The Last Decade
Jon Paul Janet
41 papers receiving 2.2k citations
Hit Papers
Peers
Comparison fields: 5 of 124
- Computational Theory and Mathematics 781
- Inorganic Chemistry 528
- Catalysis 236
- Materials Chemistry 1.6k
- Renewable Energy, Sustainability and the Environment 246
Countries citing papers authored by Jon Paul Janet
This map shows the geographic impact of Jon Paul Janet'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 Jon Paul Janet with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jon Paul Janet more than expected).
Fields of papers citing papers by Jon Paul Janet
This network shows the impact of papers produced by Jon Paul Janet. 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 Jon Paul Janet. The network helps show where Jon Paul Janet may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Jon Paul Janet, 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 | 22 | |
| 3 | 2024 | 9 | |
| 4 | 2024 | 0 | |
| 5 | 2024 | 5 | |
| 6 | 2024 | 52 | |
| 7 | 2024 | 7 | |
| 8 | Reinvent 4: Modern AI–driven generative molecule designbreakdown → | 2024 | 104 |
| 9 | 2024 | 4 | |
| 10 | 2023 | 13 | |
| 11 | 2023 | 11 | |
| 12 | 2023 | 15 | |
| 13 | 2023 | 0 | |
| 14 | 2022 | 12 | |
| 15 | 2022 | 3 | |
| 16 | 2021 | 39 | |
| 17 | 2021 | 18 | |
| 18 | Understanding the diversity of the metal-organic framework ecosystembreakdown → | 2020 | 426 |
| 19 | 2019 | 32 | |
| 20 | 2018 | 3 |
About Jon Paul Janet
Jon Paul Janet is a scholar working on Computational Theory and Mathematics, Materials Chemistry and Physical and Theoretical Chemistry, having authored 45 papers that have together received 2.2k indexed citations. Recurring topics across this work include Machine Learning in Materials Science (30 papers), Computational Drug Discovery Methods (26 papers), Protein Structure and Dynamics (14 papers), Innovative Microfluidic and Catalytic Techniques Innovation (4 papers), Electrocatalysts for Energy Conversion (4 papers), Metabolomics and Mass Spectrometry Studies (3 papers), Chemical Synthesis and Analysis (2 papers) and Molecular Junctions and Nanostructures (2 papers). The work is most often cited by research in Computational Theory and Mathematics (781 citations), Inorganic Chemistry (528 citations) and Catalysis (236 citations). Jon Paul Janet has collaborated with scholars based in Sweden, United States and United Kingdom. Frequent co-authors include Heather J. Kulik, Aditya Nandy, Chenru Duan, Tzuhsiung Yang, Ola Engkvist, Yongjin Lee, Seyed Mohamad Moosavi, Berend Smit, Peter G. Boyd and Kevin Maik Jablonka.
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.