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First-Principles Data-Driven Approach for Assessment of Stability of Tc-C systemsстатья
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: 23 января 2026 г.
- Авторы: Zaripov Radion A., Eremin Roman A., Humonen Innokentiy S., Krautsou Aliaksei V., Kuznetsov Vitaly V., German Konstantin E., Budennyy Semen A., Levchenko Sergey V.
- Журнал: Acta Materialia
- Том: 303
- Год издания: 2026
- Издательство: Elsevier BV
- Местоположение издательства: Netherlands
- Номер статьи: 121704
- DOI: 10.1016/j.actamat.2025.121704
- Аннотация: Technetium carbides (Tc–C) are of significant interest for applications in nuclear waste management and advanced reactor technologies due to their potential to immobilize radioactive technetium-99. However, a comprehensive understanding of the Tc–C system, including phase stability, structural properties, and thermodynamic behavior, remains elusive. In this work, we present a thorough theoretical investigation of technetium carbides using a hybrid approach resting upon density functional theory calculations and machine learning methods for predicting thermodynamic properties. We explore complete compositional/configurational space for the carbon concentrations up to 20.0 at.% in two Tc lattices. By analyzing energetics of carbon interstitial defects in hexagonal (320149 structures) and cubic (11937 structures) Tc, we identify the most stable atomic configurations. Furthermore, we investigate thermodynamic stability as a function of temperature using phonon calculations and accounting for configurational entropy allowing to reconcile our findings with available experimental results. The developed approach reveals atomic structures of the stable Tc–C phases, provides new insights into their stability under certain conditions, and advances the fundamental understanding of the corresponding phase transitions. Moreover, this study offers valuable guidance for computational discovery of technetium-based materials, as well as materials with more complex chemical compositions using first-principles-based approaches in a data-efficient manner.
- Добавил в систему: Кравцов Алексей Валерьевич