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  1. 2023

  2. Accuracy, Performance, and Transferability of Interparticle Potentials for Al–Cu Alloys: Comparison of Embedded Atom and Deep Machine Learning Models

    Khazieva, E., Shchelkatchev, N. M., Tipeev, A. & Ryltsev, R., 1 Dec 2023, In: Journal of Experimental and Theoretical Physics. 137, 6, p. 864-877 14 p.

    Research output: Contribution to journal › Article › peer-review

  3. First-principles calculations of the viscosity in multicomponent metallic melts: Al-Cu-Ni as a test case

    Kondratyuk, N., Ryltsev, R., Ankudinov, V. & Chtchelkatchev, N., 1 Jun 2023, In: Journal of Molecular Liquids. 380, 121751.

    Research output: Contribution to journal › Article › peer-review

  4. Description of a glass transition with immeasurable structural relaxation time

    Chtchelkatchev, N. M., Ryltsev, R. E., Mikheyenkov, A. V., Valiulin, V. E. & Polishchuk, I. Y., Apr 2023, In: Physica A: Statistical Mechanics and its Applications. 615, 128610.

    Research output: Contribution to journal › Article › peer-review

  5. Liquid–Crystal Structure Inheritance in Machine Learning Potentials for Network-Forming Systems

    Balyakin, I. A., Ryltsev, R. E. & Chtchelkatchev, N. M., 1 Mar 2023, In: JETP Letters. 117, 5, p. 370-376 7 p.

    Research output: Contribution to journal › Article › peer-review

  6. Local structure, thermodynamics, and melting of boron phosphide at high pressures by deep learning-driven ab initio simulations

    Chtchelkatchev, N. M., Ryltsev, R. E., Magnitskaya, M. V., Gorbunov, S. M., Cherednichenko, K. A., Solozhenko, V. L. & Brazhkin, V. V., 2023, In: Journal of Chemical Physics. 159, 6, 064507.

    Research output: Contribution to journal › Article › peer-review

  7. Machine learning-assisted MD simulation of melting in superheated AlCu validates the Classical Nucleation Theory

    Tipeev, A. O., Ryltsev, R. E., Chtchelkatchev, N. M., Ramprakash, S. & Zanotto, E. D., 2023, In: Journal of Molecular Liquids. 387, 122606.

    Research output: Contribution to journal › Article › peer-review

  8. СТРУКТУРНАЯ НАСЛЕДСТВЕННОСТЬ ЖИДКОСТЬ-КРИСТАЛЛ В ПОТЕНЦИАЛАХ МАШИННОГО ОБУЧЕНИЯ ДЛЯ СЕТЕОБРАЗУЮЩИХ СИСТЕМ

    Балякин, И. А., Рыльцев, Р. Е. & Щелкачев, Н. М., 2023, In: Письма в Журнал экспериментальной и теоретической физики. 117, 5-6 (3), p. 377-384 8 p.

    Research output: Contribution to journal › Article › peer-review

  9. ТОЧНОСТЬ, ПРОИЗВОДИТЕЛЬНОСТЬ И ПЕРЕНОСИМОСТЬ МЕЖЧАСТИЧНЫХ ПОТЕНЦИАЛОВ ДЛЯ СПЛАВОВ AL-CU: СРАВНЕНИЕ МОДЕЛЕЙ ПОГРУЖЕННОГО АТОМА И ГЛУБОКОГО МАШИННОГО ОБУЧЕНИЯ

    Хазиева, Е. О., Щелкачев, Н. М., Типеев, А. О. & Рыльцев, Р. Е., 2023, In: Журнал экспериментальной и теоретической физики. 164, 6, p. 980-995 6 p.

    Research output: Contribution to journal › Article › peer-review

  10. 2022

  11. Ground-state structure, orbital ordering and metal-insulator transition in double-perovskite PrBaMn2O6

    Streltsov, S. V., Ryltsev, R. E. & Chtchelkatchev, N. M., 15 Aug 2022, In: Journal of Alloys and Compounds. 912, 165150.

    Research output: Contribution to journal › Article › peer-review

  12. Deep machine learning potentials for multicomponent metallic melts: Development, predictability and compositional transferability

    Ryltsev, R. E. & Chtchelkatchev, N. M., 1 Mar 2022, In: Journal of Molecular Liquids. 349, 10 p., 118181.

    Research output: Contribution to journal › Article › peer-review

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