Finite‐temperature properties of NbO2 from a deep‐learning interatomic potential

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Finite‐temperature properties of NbO2 from a deep‐learning interatomic potential

Abstract Using first‐principles‐based machine‐learning potential, molecular dynamics (MD) simulations are performed to investigate the micro‐mechanism in phase transition of NbO2. Treating the DFT results of the low‐ and intermediate‐temperature phases of NbO2 as training data in the deep‐learning m...

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Bibliografiska uppgifter
Huvudupphovsmän:	Xinhang Li, Yongqiang Wang, Tianyu Jiao, Zhaoxin Liu, Chuanle Yang, Ri He, Liang Si
Materialtyp:	Artikel
Språk:	engelska
Publicerad:	Wiley-VCH 2025-06-01
Serie:	Materials Genome Engineering Advances
Ämnen:	deep‐learning model density functional theory interatomic potential molecular dynamics phase transitions
Länkar:	https://doi.org/10.1002/mgea.70011
Taggar:	Lägg till en tagg Inga taggar, Lägg till första taggen!

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