Maksim Kulichenko

992 total citations
28 papers, 689 citations indexed

About

Maksim Kulichenko is a scholar working on Materials Chemistry, Organic Chemistry and Inorganic Chemistry. According to data from OpenAlex, Maksim Kulichenko has authored 28 papers receiving a total of 689 indexed citations (citations by other indexed papers that have themselves been cited), including 20 papers in Materials Chemistry, 7 papers in Organic Chemistry and 7 papers in Inorganic Chemistry. Recurrent topics in Maksim Kulichenko's work include Machine Learning in Materials Science (8 papers), Boron and Carbon Nanomaterials Research (6 papers) and Spectroscopy and Quantum Chemical Studies (5 papers). Maksim Kulichenko is often cited by papers focused on Machine Learning in Materials Science (8 papers), Boron and Carbon Nanomaterials Research (6 papers) and Spectroscopy and Quantum Chemical Studies (5 papers). Maksim Kulichenko collaborates with scholars based in United States, Russia and Chile. Maksim Kulichenko's 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 and has published in prestigious journals such as Chemical Reviews, Angewandte Chemie International Edition and The Journal of Chemical Physics.

In The Last Decade

Maksim Kulichenko

26 papers receiving 679 citations

Peers

Maksim Kulichenko

AMPO

CTM

Nikita Fedik United States

Makito Takagi Japan

Yosuke Sumiya Japan

José Manuel Vásquez‐Pérez Mexico

Said Jalife Mexico

Santanu Saha Switzerland

Gourhari Jana India

Wanrun Jiang China

Chien‐Pin Chou Taiwan

Stephen G. Dale Canada

Nikita Fedik United States

Maksim Kulichenko

433 ×0.9

250 ×1.7

154 ×1.4

123 ×1.2

AMPO

77 ×0.8

CTM

Citations per year, relative to Maksim Kulichenko Maksim Kulichenko (= 1×) peers Nikita Fedik

Countries citing papers authored by Maksim Kulichenko

Since Specialization

Citations

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

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

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20 of 20 papers shown

Li, Cheng-Han, Mehmet Cagri Kaymak, Maksim Kulichenko, et al.. (2025). Shadow Molecular Dynamics with a Machine Learned Flexible Charge Potential. Journal of Chemical Theory and Computation. 21(7). 3658–3675. 1 indexed citations

Athavale, Vishikh, et al.. (2025). PYSEQM 2.0: Accelerated Semiempirical Excited-State Calculations on Graphical Processing Units. Journal of Chemical Theory and Computation. 21(19). 9498–9510. 2 indexed citations

Kulichenko, Maksim, Benjamin Nebgen, Nicholas Lubbers, et al.. (2024). Data Generation for Machine Learning Interatomic Potentials and Beyond. Chemical Reviews. 124(24). 13681–13714. 55 indexed citations

Chen, Weijia, et al.. (2023). On the structures and bonding of copper boride nanoclusters, Cu2B– (x = 5–7). Solid State Sciences. 142. 107248–107248. 7 indexed citations

Freixas, Victor M., Walter Malone, Xinyang Li, et al.. (2023). NEXMD v2.0 Software Package for Nonadiabatic Excited State Molecular Dynamics Simulations. Journal of Chemical Theory and Computation. 19(16). 5356–5368. 18 indexed citations

Fedik, Nikita, Benjamin Nebgen, Nicholas Lubbers, et al.. (2023). Synergy of semiempirical models and machine learning in computational chemistry. The Journal of Chemical Physics. 159(11). 12 indexed citations

Chen, Weijia, et al.. (2023). Photoelectron Spectroscopy and Theoretical Study of Di-Copper–Boron Clusters: Cu₂B₃^– and Cu₂B₄^–. The Journal of Physical Chemistry A. 127(22). 4888–4896. 13 indexed citations

Kulichenko, Maksim, Kipton Barros, Nicholas Lubbers, et al.. (2023). Uncertainty-driven dynamics for active learning of interatomic potentials. Nature Computational Science. 3(3). 230–239. 87 indexed citations

Fedik, Nikita, R.I. Zubatyuk, Maksim Kulichenko, et al.. (2022). Extending machine learning beyond interatomic potentials for predicting molecular properties. Nature Reviews Chemistry. 6(9). 653–672. 94 indexed citations

10.

Kulichenko, Maksim, Justin S. Smith, Benjamin Nebgen, et al.. (2021). The Rise of Neural Networks for Materials and Chemical Dynamics. The Journal of Physical Chemistry Letters. 12(26). 6227–6243. 66 indexed citations

11.

Kulichenko, Maksim, et al.. (2021). Designing Molecular Electrides from Defective Unit Cells of Cubic Alkaline Earth Oxides. The Journal of Physical Chemistry C. 125(17). 9564–9570. 4 indexed citations

12.

Tkachenko, Nikolay V., Ivan A. Popov, Maksim Kulichenko, et al.. (2021). Bridging Aromatic/Antiaromatic Units: Recent Advances in Aromaticity and Antiaromaticity in Main‐Group and Transition‐Metal Clusters from Bonding and Magnetic Analyses. European Journal of Inorganic Chemistry. 2021(41). 4239–4250. 21 indexed citations

13.

Fedik, Nikita, Maksim Kulichenko, Д. В. Стегленко, & Alexander I. Boldyrev. (2020). Can aromaticity be a kinetic trap? Example of mechanically interlocked aromatic [2-5]catenanes built from cyclo[18]carbon. Chemical Communications. 56(18). 2711–2714. 54 indexed citations

14.

Kulichenko, Maksim, Nikita Fedik, Alvaro Muñoz‐Castro, et al.. (2020). “Bottled” spiro-doubly aromatic trinuclear [Pd₂Ru]⁺complexes. Chemical Science. 12(1). 477–486. 18 indexed citations

15.

Kulichenko, Maksim, Nikita Fedik, K. V. Bozhenko, & Alexander I. Boldyrev. (2019). Inorganic Molecular Electride Mg₄O₃: Structure, Bonding, and Nonlinear Optical Properties. Chemistry - A European Journal. 25(20). 5311–5315. 19 indexed citations

16.

Czekner, Joseph, et al.. (2019). High‐Resolution Photoelectron Imaging of IrB₃⁻: Observation of a π‐Aromatic B₃⁺ Ring Coordinated to a Transition Metal. Angewandte Chemie. 131(26). 8969–8973. 5 indexed citations

17.

Fedik, Nikita, Maksim Kulichenko, & Alexander I. Boldyrev. (2019). Two names of stability: Spherical aromatic or superatomic intermetalloid cluster [Pd3Sn8Bi6]4−. Chemical Physics. 522. 134–137. 16 indexed citations

18.

Kulichenko, Maksim, Nikita Fedik, K. V. Bozhenko, & Alexander I. Boldyrev. (2019). Hydrated Sulfate Clusters SO₄^2–(H₂O)_n (n = 1–40): Charge Distribution Through Solvation Shells and Stabilization. The Journal of Physical Chemistry B. 123(18). 4065–4069. 38 indexed citations

19.

Kulichenko, Maksim, Nikita Fedik, Alexander I. Boldyrev, & Alvaro Muñoz‐Castro. (2019). Expansion of Magnetic Aromaticity Criteria to Multilayer Structures: Magnetic Response and Spherical Aromaticity of Matryoshka‐Like Cluster [Sn@Cu₁₂@Sn₂₀]¹²⁻. Chemistry - A European Journal. 26(10). 2263–2268. 29 indexed citations

20.

Kulichenko, Maksim, et al.. (1965). Measurement of radioactivity at the surface of aqueous solutions. Atomic Energy. 18(3). 380–382. 1 indexed citations

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.

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