Data-Driven Prediction of Structures of Metal–Organic Frameworks

Авторы: Yakovenko Elizaveta I., Nevolin Iurii M., Chasovskikh Anatoliy A., Mitrofanov Artem A., Korolev Vadim V.
Журнал: Journal of Chemical Information and Modeling
Том: 65
Номер: 4
Год издания: 2025
Издательство: American Chemical Society
Местоположение издательства: United States
Первая страница: 1718
Последняя страница: 1723
DOI: 10.1021/acs.jcim.4c02367
Аннотация: Crystal structure prediction (CSP) has proven to be an effective route for the discovery of new materials. Nonetheless, the ab initio techniques employed for the CSP of metal–organic frameworks (MOFs) cannot be scaled to a high-throughput mode. Here, we propose a data-driven method for addressing the current needs of computational MOF discovery. Specifically, coarse-grained neural networks were implemented to predict the underlying net topology. The models showed satisfactory performance, which was next enhanced via the limitation of the applicability domain.
Добавил в систему: Королев Вадим Викторович

	ИСТИНА	Войти в систему Регистрация
	ФНКЦ РР
	Главная Поиск Статистика О проекте Помощь

ИСТИНА