Computational Chemistry and Chemical Design
This area includes the use of quantum chemical approaches and artificial intelligence methods to solve various chemical problems.
Main scientific results:
- A model based on quantum chemical calculations has been developed to predict the stability of organic molecules against gamma irradiation.
- A model based on artificial intelligence has been developed to suggest the composition and spatial group of materials with a given target property.
- An algorithm for DFT-functional optimization for specific chemical problems has been developed.
- A reference database with the results of relativistic ab initio modeling for simple actinide compounds has been published.
- Quantitative models based on machine learning have been developed in order to estimate stability constants of complexes of trivalent f-elements with various organic ligands.
|1. A. Mitrofanov, V. Korolev, N. Andreadi, V. Petrov, S. Kalmykov. A simple automatized tool for exchange-correlation functional fitting // The journal of physical chemistry A, 2020, DOI: 10.1021/acs.inorgchem.0c01746 (IF: 2.6)|
|2. 10.1039/D0CP01786H (IF: 3.43)A search of a quantitative quantum-chemical approach for radiolytic stability prediction // Physical Chemistry Chemical Physics, 2020, DOI:|
|3. N. Andreadi, A. Mitrofanov, P. Matveev, A. Volkova, and S. Kalmykov. Heavy-element reactions database (herdb): Relativistic ab initio geometries and energies for actinide compounds // Inorganic Chemistry, 2020, DOI: 10.1021/acs.inorgchem.0c01746 (IF: 4.825)|
|4. V. Korolev, A. Mitrofanov, A. Eliseev, V. Tkachenko. Machine-learning-assisted search for functional materials over extended chemical space // Materials Horizons, 2020, DOI: 10.1039/D0MH00881H (IF: 12.319)|
|5. V.V. Korolev, A. Mitrofanov, E.I. Marchenko, N.N. Eremin, V. Tkachenko, and S.N. Kalmykov. Transferable and extensible machine learning derived atomic charges for modeling hybrid nanoporous materials // Chemistry of Materials. , 2020, DOI: 10.1021/acs.chemmater.0c02468 (IF: 9.567)|
|6. P.I. Matveev, A.A. Mitrofanov, V.G. Petrov, S.S. Zhokhov, A.A. Smirnova, Yu.A. Ustynyuka, S.N. Kalmykov. Testing a simple approach for theoretical evaluation of radiolysis products in extraction systems. A case of N,O-donor ligands for Am/Eu separation // RSC advances, 2017, DOI: 10.1039/C7RA11622E (IF: 3.108)|
|7. N.N. Eremin, E.I. Marchenko, V.G. Petrov, A.A. Mitrofanov, A.S. Ulanova. Solid solutions of monazites and xenotimes of lanthanides and plutonium: Atomistic model of crystal structures, point defects and mixing properties // Computational Materials Science, 2019, DOI: 10.1016/j.commatsci.2018.10.025 (IF: 2.863)|