Exploring the structural diversity and energy landscape of CrSi2N4

The energy landscape of CrSi2N4 was explored using Global optimization, Data mining, and the Primitive Cell for Atom Exchange method (PCAE), generating ten promising structural candidates [1]. Similar to the previously investigated Cr2SiN4 system [2], data mining identified the global minimum (TiMn2O4 structure type) with the lowest energy, confirmed using both, the GGA-PBE and LDA-PZ functionals. Seven other favorable modifications were found through global optimization, and one each through data mining and the PCAE. Decomposition into CrN, Si3N4, and N2 was also analyzed, revealing that the global minimum phase remains stable at low temperatures. Due to the absence of experimental data, two functionals were employed to validate the energy rankings and structural predictions, demonstrating strong agreement. These results suggest the identified candidates are likely (meta)stable phases of CrSi2N4, offering promising targets for future experimental synthesis and further studies on their properties and applications.

Publication Details

Journal
VinaR (Institute of Nuclear Sciences "Vinča")
Published
2025-01-01
Primary Topic
Machine Learning in Materials Science
Type
article
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article

Exploring the structural diversity and energy landscape of CrSi2N4

VinaR (Institute of Nuclear Sciences "Vinča")
Machine Learning in Materials Science
article

Exploring the structural diversity and energy landscape of CrSi2N4

article en

Abstract

The energy landscape of CrSi2N4 was explored using Global optimization, Data mining, and the Primitive Cell for Atom Exchange method (PCAE), generating ten promising structural candidates [1]. Similar to the previously investigated Cr2SiN4 system [2], data mining identified the global minimum (TiMn2O4 structure type) with the lowest energy, confirmed using both, the GGA-PBE and LDA-PZ functionals. Seven other favorable modifications were found through global optimization, and one each through data mining and the PCAE. Decomposition into CrN, Si3N4, and N2 was also analyzed, revealing that the global minimum phase remains stable at low temperatures. Due to the absence of experimental data, two functionals were employed to validate the energy rankings and structural predictions, demonstrating strong agreement. These results suggest the identified candidates are likely (meta)stable phases of CrSi2N4, offering promising targets for future experimental synthesis and further studies on their properties and applications.

VinaR (Institute of Nuclear Sciences "Vinča")
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Machine Learning in Materials Science
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Exploring the structural diversity and energy landscape of CrSi2N4 · VinaR (Institute of Nuclear Sciences "Vinča") (2025) | TGRS Research Map | TGRS