Application of Machine Learning for Predicting Phase Behavior of Interpolyelectrolyte Complexes in Water–Salt Media
Water–salt solutions of interpolyelectrolyte complexes (IPECs) represent a classical example of “smart” systems, the phase equilibrium in which is regulated by many factors associated with both the parameters of the polymer components and the physical and chemical properties of an environment. This paper presents a model created on the basis of machine learning for predicting the region of existence of water-soluble IPECs. An approach is proposed for independent account of the physicochemical properties of polyelectrolytes and the properties of the environment. The developed model is universal and can be used to predict the properties of multicomponent systems of various chemical natures.
Authors
- Andrey V. Sybachin (ORCID: https://orcid.org/0000-0002-4860-5565)
- A S Grigoryan (ORCID: https://orcid.org/0000-0002-0123-8235)
- Andrei A. Korigodskii (ORCID: https://orcid.org/0000-0002-3174-4577)
- Vladislava A. Pigareva (ORCID: https://orcid.org/0000-0002-3989-2232)
- I. V. Grigoryan
- A.V. Tishchenko
- Г.Б. Хомутов (ORCID: https://orcid.org/0000-0002-7639-5171)
- Liubov A Antiufrieva
- C. Junyang
- Y. Shuxiong
Institutions
- Skolkovo Institute of Science and Technology (RU)
- Lomonosov Moscow State University (RU)
- A. N. Nesmeyanov Institute of Organoelement Compounds (RU)
- Moscow State University (TJ)
- Kotelnikov Institute of Radioengineering and Electronics of the Russian Academy of Sciences (RU)
Publication Details
- Journal
- Colloid Journal
- Published
- 2025-05-28
- DOI
- https://doi.org/10.1134/s1061933x25600137
- Primary Topic
- Machine Learning in Materials Science
- Type
- article
- Field-Weighted Citation Impact
- 0.00