Neural network-based order parameter for phase transitions and its applications in high-entropy alloys.
Nat Comput Sci
; 1(10): 686-693, 2021 Oct.
Article
em En
| MEDLINE
| ID: mdl-38217201
ABSTRACT
Phase transition is one of the most important phenomena in nature and plays a central role in materials design. All phase transitions are characterized by suitable order parameters, including the order-disorder phase transition. However, finding a representative order parameter for complex systems is non-trivial, such as for high-entropy alloys. Given the strength of dimensionality reduction of a variational autoencoder (VAE), we introduce a VAE-based order parameter. We propose that the Manhattan distance in the VAE latent space can serve as a generic order parameter for order-disorder phase transitions. The physical properties of our order parameter are quantitatively interpreted and demonstrated by multiple refractory high-entropy alloys. Using this order parameter, a generally applicable alloy design concept is proposed by mimicking the natural mixing process of elements. Our physically interpretable VAE-based order parameter provides a computational technique for understanding chemical ordering in alloys, which can facilitate the development of rational alloy design strategies.
Texto completo:
1
Base de dados:
MEDLINE
Idioma:
En
Revista:
Nat Comput Sci
Ano de publicação:
2021
Tipo de documento:
Article
País de afiliação:
Estados Unidos