Alexei A. Lapkin
- Materials Chemistry top 1%
- Biomedical Engineering top 0.5%
- Renewable Energy, Sustainability and the Environment top 1%
- Mechanical Engineering top 2%
- Organic Chemistry top 2%
- Co-authors
- Dmitry V. BavykinFrank C. WalshArtur M. SchweidtmannPaweł PlucińskiValentin N. ParmonYulong DingHaisheng ChenEric Bradford
- Topics
- Innovative Microfluidic and Catalytic Techniques Innovation (36 papers)Computational Drug Discovery Methods (31 papers)Machine Learning in Materials Science (29 papers)
- Cited by
- CatalysisRenewable Energy, Sustainability and the EnvironmentProcess Chemistry and Technology
- Partner nations
- United KingdomSingaporeGermany
In The Last Decade
Alexei A. Lapkin
194 papers receiving 7.5k citations
Hit Papers
Peers
Comparison fields: 5 of 176
- Materials Chemistry 2.9k
- Biomedical Engineering 2.6k
- Renewable Energy, Sustainability and the Environment 1.6k
- Mechanical Engineering 1.1k
- Organic Chemistry 1.1k
Countries citing papers authored by Alexei A. Lapkin
This map shows the geographic impact of Alexei A. Lapkin'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 Alexei A. Lapkin with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Alexei A. Lapkin more than expected).
Fields of papers citing papers by Alexei A. Lapkin
This network shows the impact of papers produced by Alexei A. Lapkin. 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 Alexei A. Lapkin. The network helps show where Alexei A. Lapkin may publish in the future.
Co-authorship network of co-authors of Alexei A. Lapkin
This figure shows the co-authorship network connecting the top 25 collaborators of Alexei A. Lapkin. A scholar is included among the top collaborators of Alexei A. Lapkin 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 Alexei A. Lapkin. Alexei A. Lapkin 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 | 3 | |
| 3 | 2 | |
| 4 | 1 | |
| 5 | 0 | |
| 6 | 2 | |
| 7 | 2 | |
| 8 | 42 | |
| 9 | A Brief Introduction to Chemical Reaction Optimizationbreakdown → | 238 |
| 10 | 25 | |
| 11 | 5 | |
| 12 | 64 | |
| 13 | 7 | |
| 14 | 2 | |
| 15 | 34 | |
| 16 | 40 | |
| 17 | 25 | |
| 18 | 33 | |
| 19 | 11 | |
| 20 | 39 |
About Alexei A. Lapkin
Alexei A. Lapkin is a scholar working on Catalysis, Process Chemistry and Technology and Computational Theory and Mathematics, having authored 201 papers that have together received 7.6k indexed citations. Recurring topics across this work include Innovative Microfluidic and Catalytic Techniques Innovation (36 papers), Computational Drug Discovery Methods (31 papers) and Machine Learning in Materials Science (29 papers). The work is most often cited by research in Catalysis (834 citations), Renewable Energy, Sustainability and the Environment (1.6k citations) and Process Chemistry and Technology (198 citations). Alexei A. Lapkin has collaborated with scholars based in United Kingdom, Singapore and Germany. Frequent co-authors include Dmitry V. Bavykin, Frank C. Walsh, Artur M. Schweidtmann, Paweł Pluciński, Valentin N. Parmon, Yulong Ding, Haisheng Chen, Eric Bradford, Jens M. Friedrich and Xiaolei Fan. Their work appears in journals such as Chemical Reviews, Chemical Society Reviews 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.