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Researcher · Insilicos (United States) · United States

Maksim Kuznetsov

Maksim Kuznetsov publishes mostly in Materials Chemistry, Molecular Biology and Computational Theory and Mathematics, on topics such as Machine Learning in Materials Science, Computational Drug Discovery Methods and Protein Structure and Dynamics.

ORCID 0000-0001-8446-7983 OpenAlex
World rank
#2,575,290
of 7,710,434 ranked researchers
Rank in United States
#756,371
of 1,627,098
Works
17
Citations
1.8k
Citations per work
103.4
h-index
5
i10-index 5Top 94% in Materials Chemistry
Maksim Kuznetsov

What does Maksim Kuznetsov research?

Maksim Kuznetsov's work is mostly in Computer Science (24%), Materials Science (24%), Biochemistry, Genetics and Molecular Biology (22%).

Physical Sciences: 10 works · 58.6% of their workComputer Science: 4 works · 24.4% of their workArtificial Intelligence: 2 works · 12.2% of their workComputational Theory and Mathematics: 2 works · 12.2% of their workMaterials Science: 4 works · 24.4% of their workMaterials Chemistry: 4 works · 24.4% of their workEngineering: 2 works · 9.8% of their workBiomedical Engineering: 1 works · 4.9% of their workMechanical Engineering: 1 works · 4.9% of their workLife Sciences: 4 works · 22.0% of their workBiochemistry, Genetics and Molecular Biology: 4 works · 22.0% of their workMolecular Biology: 4 works · 22.0% of their workEverything else: 3 works · 19.4% of their workTHEIR WORKMaksim Kuznetsov17works
Life Sciences22%Physical Sciences59%

Their six largest subfields, inside their fields and domains; the grey slice is everything else they publish. The interactive view splits every one of their works down to single topics.

The fields and subfields, with their numbers
    1. Computer Science424.4%
    2. Materials Science424.4%
    3. Engineering29.8%
    1. Biochemistry, Genetics and Molecular Biology422.0%
  3. Everything else319.4%
  1. 1Materials Chemistry24% of their works
  2. 2Molecular Biology22% of their works
  3. 3Computational Theory and Mathematics12% of their works
  4. 4Artificial Intelligence12% of their works
  5. 5Biomedical Engineering5% of their works
  6. 6Mechanical Engineering5% of their works

Research topics

Which keywords describe Maksim Kuznetsov's research?

The keywords of their largest research topics: Machine Learning, Molecular Dynamics, High-Throughput, Materials Informatics, Molecular Docking, Virtual Screening, Neural Networks and Protein.

Size is their works in the topics tagged with each word, from their 8 largest topics. Each links to the topic it comes from most. The words come from the keyword list OpenAlex gives each topic, not from the text of the papers, so a word can appear that none of the papers uses.

All 18 words, with their numbers
  1. Machine Learning143 topics
  2. Molecular Dynamics112 topics
  3. High-Throughput81 topic
  4. Materials Informatics81 topic
  5. Molecular Docking51 topic
  6. Virtual Screening51 topic
  7. Neural Networks31 topic
  8. Protein31 topic
  9. Structure31 topic
  10. Word Representation31 topic
  11. Biomedical Ontologies21 topic
  12. Droplet Formation21 topic
  13. Microfluidics21 topic
  14. Modular Robots21 topic
  15. Self-Reconfiguration21 topic
  16. Text Mining21 topic
  17. Bioimage Analysis11 topic
  18. High-Content Screening11 topic

Where does Maksim Kuznetsov work?

Maksim Kuznetsov's main affiliation in the publication record is Insilicos (United States), United States.

How many publications and citations does Maksim Kuznetsov have?

OpenAlex counts 17 works and 1,757 citations for Maksim Kuznetsov, an h-index of 5, ranking #2,575,290 of 7,710,434 researchers worldwide on the quality score.

Ranked on field-normalised citation excellence (50%), output (30%) and citations (20%). Counts come from OpenAlex author records, which occasionally merge different people who share a name or split one person into several.

Papers, co-authors, who cited this work and researchers on the nearest topics have tabs of their own, above. Is this your page? Claim it with your ORCID iD, or request a correction or removal.

Discussion

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