Using of Machine Learning Capabilities to Predict Double Phosphate Structures for Biomedical Applications

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Resumo

In the rapidly developing field of biomedical research, the search for new materials with improved properties is crucial to moving the entire field forward. Double phosphates have generated significant interest in a wide range of applications, ranging from drug delivery systems to catalysts for biomedical reactions, and the fields of biomedicine and tissue engineering are no exception. In this article, we propose a method for finding new double phosphate materials based on machine learning, screening and applying data from structural databases, and we use this methodology combined with chemical knowledge to propose several promising materials for bone engineering. For the selected candidates, we develop a solid-phase synthesis procedure and apply physical characteristics to confirm the results. In addition, the role of microstructure, i.e. The porosity of frameworks based on these materials is discussed from a biomedical point of view, and several synthetic ways to adjust this parameter are proposed and investigated.

Sobre autores

E. Kolomenskaya

Southern Federal University

Autor responsável pela correspondência
Email: kolomenskaya@sfedu.ru
Rússia, Rostov-on-Don

V. Butova

Southern Federal University; Institute of General and Inorganic Chemistry of the Bulgarian Academy of Sciences

Email: kolomenskaya@sfedu.ru
Rússia, Rostov-on-Don; Sofia, Bulgaria

Yu. Rusalev

Southern Federal University

Email: kolomenskaya@sfedu.ru
Rússia, Rostov-on-Don

B. Protsenko

Southern Federal University

Email: kolomenskaya@sfedu.ru
Rússia, Rostov-on-Don

A. Soldatov

Southern Federal University

Email: kolomenskaya@sfedu.ru
Rússia, Rostov-on-Don

M. Butakova

Southern Federal University

Email: kolomenskaya@sfedu.ru
Rússia, Rostov-on-Don

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