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Prediction of Metal–Organic Frameworks with Phase Transition via Machine Learningстатья
Статья опубликована в высокорейтинговом журнале
Информация о цитировании статьи получена из
Scopus
Статья опубликована в журнале из списка Web of Science и/или Scopus
Дата последнего поиска статьи во внешних источниках: 10 апреля 2024 г.
- Авторы: Karsakov Grigory V., Shirobokov Vladimir P., Kulakova Alena, Milichko Valentin A.
- Журнал: Journal of Physical Chemistry Letters
- Том: 15
- Номер: 11
- Год издания: 2024
- Издательство: American Chemical Society
- Местоположение издательства: United States
- Первая страница: 3089
- Последняя страница: 3095
- DOI: 10.1021/acs.jpclett.3c03639
- Аннотация: Metal–organic frameworks (MOFs) possess a virtually unlimited number of potential structures. Although the latter enables an efficient route to control the structure-related functional properties of MOFs, it still complicates the prediction and searching for an optimal structure for specific application. Next to prediction of the MOFs for gas sorption/separation and catalysis via machine learning (ML), we report on ML to find MOFs demonstrating a phase transition (PT). On the basis of an available QMOF database (7463 frameworks), we create and train the autoencoder followed by training the classifier of MOFs from a unique database with experimentally confirmed PT. This makes it possible to identify MOFs with a high potential for PT and evaluate the most likely stimulus for it (guest molecules or temperature/pressure). The formed list of available MOFs for PT allows us to discuss their structural features and opens an opportunity to search for phase change MOFs for diverse physical/chemical application.
- Добавил в систему: Астахов Григорий Сергеевич