XVII Международный Российско-Китайский Симпозиум
НОВЫЕ МАТЕРИАЛЫ И ТЕХНОЛОГИИ
18 – 22 августа 2025 г.
Екатеринбург
Valentine P. Ananikov
Zelinsky Institute of Organic Chemistry, Russian Academy of Sciences
Leninsky prospekt 47, Moscow; http://AnanikovLab.ru
*e-mail:val@ioc.ac.ru
Integration of artificial intelligence (AI) algorithms into chemical research has become increasingly pivotal due to the rapid growth in experimental data volumes, the complexity of correlating phenomena at molecular and nanoscale levels, and the urgent need for a qualitative leap in the development of universal chemical technologies [1]. This report focuses on the application of AI in the field of materials design, specifically in the design of new materials for catalysts. We explore cutting-edge trends in the development of AI applications for the creation of next-generation experimental methods [2,3], the analysis of experimental data [4], and the understanding of the mechanisms of formation and operation of chemical micro-reactors [5]. Particular emphasis is placed on the use of AI to develop universal photocatalysts [6], highlighting how these technologies enable unprecedented precision and efficiency in catalyst design. This approach not only promises to enhance current catalytic processes but also to discover novel catalysts with optimized activity and selectivity. In this presentation, single-atom and nano-scale catalytic systems studied with artificial intelligence will be the main focus.
Acknowledgment. Supported by the Russian Science Foundation (grant 25-13-00387).
References
[1] Ananikov, V.P. Top 20 Influential AI-Based Technologies in Chemistry. Artificial Intelligence Chemistry, 2024, 100075. https://doi.org/10.1016/j.aichem.2024.100075
[2] Galushko, A.S.; Boiko, D.A.; Pentsak, E.O.; Eremin, D.B.; Ananikov, V. P. J. Am. Chem. Soc., 2023, 145, 9092. https://doi.org/10.1021/jacs.3c00645
[3] Eremin, D.B.; Galushko, A.S.; Boiko, D.A.; Pentsak, E.O.; Chistyakov, I.V.; Ananikov, V. P. J. Am. Chem. Soc., 2022, 144, 13, 6071–6079. https://doi.org/10.1021/jacs.2c01283
[4] Boiko, D.A.; Kozlov, K.S.; Burykina, Yu.V.; Ilyushenkova, V.V.; Ananikov, V.P. J. Am. Chem. Soc., 2022, 144, 32, 14590-14606. https://doi.org/10.1021/jacs.2c03631
[5] Kashin, A.S.; Prima, D.O.; Arkhipova, D.M.; Ananikov, V. P. Small, 2023, 2302999. https://doi.org/10.1002/smll.202302999
[6] Ghosh, I.; Shlapakov, N.S.; Karl, T.A.; Düker, J.; Nikitin, M.; Burykina, J.V.; Ananikov, V.P.; König, B. Nature, 2023, 619, 87–93. https://doi.org/10.1038/s41586-023-06087-4
Zelinsky Institute of Organic Chemistry, Russian Academy of Sciences
Leninsky prospekt 47, Moscow; http://AnanikovLab.ru
*e-mail:val@ioc.ac.ru
Integration of artificial intelligence (AI) algorithms into chemical research has become increasingly pivotal due to the rapid growth in experimental data volumes, the complexity of correlating phenomena at molecular and nanoscale levels, and the urgent need for a qualitative leap in the development of universal chemical technologies [1]. This report focuses on the application of AI in the field of materials design, specifically in the design of new materials for catalysts. We explore cutting-edge trends in the development of AI applications for the creation of next-generation experimental methods [2,3], the analysis of experimental data [4], and the understanding of the mechanisms of formation and operation of chemical micro-reactors [5]. Particular emphasis is placed on the use of AI to develop universal photocatalysts [6], highlighting how these technologies enable unprecedented precision and efficiency in catalyst design. This approach not only promises to enhance current catalytic processes but also to discover novel catalysts with optimized activity and selectivity. In this presentation, single-atom and nano-scale catalytic systems studied with artificial intelligence will be the main focus.
Acknowledgment. Supported by the Russian Science Foundation (grant 25-13-00387).
References
[1] Ananikov, V.P. Top 20 Influential AI-Based Technologies in Chemistry. Artificial Intelligence Chemistry, 2024, 100075. https://doi.org/10.1016/j.aichem.2024.100075
[2] Galushko, A.S.; Boiko, D.A.; Pentsak, E.O.; Eremin, D.B.; Ananikov, V. P. J. Am. Chem. Soc., 2023, 145, 9092. https://doi.org/10.1021/jacs.3c00645
[3] Eremin, D.B.; Galushko, A.S.; Boiko, D.A.; Pentsak, E.O.; Chistyakov, I.V.; Ananikov, V. P. J. Am. Chem. Soc., 2022, 144, 13, 6071–6079. https://doi.org/10.1021/jacs.2c01283
[4] Boiko, D.A.; Kozlov, K.S.; Burykina, Yu.V.; Ilyushenkova, V.V.; Ananikov, V.P. J. Am. Chem. Soc., 2022, 144, 32, 14590-14606. https://doi.org/10.1021/jacs.2c03631
[5] Kashin, A.S.; Prima, D.O.; Arkhipova, D.M.; Ananikov, V. P. Small, 2023, 2302999. https://doi.org/10.1002/smll.202302999
[6] Ghosh, I.; Shlapakov, N.S.; Karl, T.A.; Düker, J.; Nikitin, M.; Burykina, J.V.; Ananikov, V.P.; König, B. Nature, 2023, 619, 87–93. https://doi.org/10.1038/s41586-023-06087-4
