Maksim Kulichenko
- Materials Chemistry top 10%
- Organic Chemistry
- Inorganic Chemistry top 10%
- Atomic and Molecular Physics, and Optics
- Computational Theory and Mathematics top 5%
- Co-authors
- Alexander I. BoldyrevNikita FedikSergei TretiakBenjamin NebgenYing Wai LiNicholas LubbersKipton BarrosJustin S. Smith
- Topics
- Machine Learning in Materials Science (8 papers)Boron and Carbon Nanomaterials Research (6 papers)Spectroscopy and Quantum Chemical Studies (5 papers)
- Partner nations
- United StatesRussiaChile
In The Last Decade
Maksim Kulichenko
26 papers receiving 679 citations
Peers
Comparison fields: 5 of 70
- Materials Chemistry 458
- Organic Chemistry 149
- Inorganic Chemistry 113
- Atomic and Molecular Physics, and Optics 101
- Computational Theory and Mathematics 101
Countries citing papers authored by Maksim Kulichenko
This map shows the geographic impact of Maksim Kulichenko'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 Maksim Kulichenko with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Maksim Kulichenko more than expected).
Fields of papers citing papers by Maksim Kulichenko
This network shows the impact of papers produced by Maksim Kulichenko. 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 Maksim Kulichenko. The network helps show where Maksim Kulichenko may publish in the future.
Co-authorship network of co-authors of Maksim Kulichenko
This figure shows the co-authorship network connecting the top 25 collaborators of Maksim Kulichenko. A scholar is included among the top collaborators of Maksim Kulichenko 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 Maksim Kulichenko. Maksim Kulichenko is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 2 | |
| 3 | 55 | |
| 4 | 7 | |
| 5 | 18 | |
| 6 | 12 | |
| 7 | 13 | |
| 8 | 87 | |
| 9 | 94 | |
| 10 | 66 | |
| 11 | 4 | |
| 12 | 21 | |
| 13 | 54 | |
| 14 | 18 | |
| 15 | 19 | |
| 16 | 5 | |
| 17 | 16 | |
| 18 | 38 | |
| 19 | 29 | |
| 20 | 1 |
About Maksim Kulichenko
Maksim Kulichenko is a scholar working on Inorganic Chemistry, Filtration and Separation and Materials Chemistry, having authored 28 papers that have together received 689 indexed citations. Recurring topics across this work include Machine Learning in Materials Science (8 papers), Boron and Carbon Nanomaterials Research (6 papers) and Spectroscopy and Quantum Chemical Studies (5 papers). The work is most often cited by research in Materials Chemistry (458 citations), Inorganic Chemistry (113 citations) and Computational Theory and Mathematics (101 citations). Maksim Kulichenko has collaborated with scholars based in United States, Russia and Chile. Frequent co-authors include Alexander I. Boldyrev, Nikita Fedik, Sergei Tretiak, Benjamin Nebgen, Ying Wai Li, Nicholas Lubbers, Kipton Barros, Justin S. Smith, Richard A. Messerly and Lai‐Sheng Wang. Their work appears in journals such as Chemical Reviews, Angewandte Chemie International Edition 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.