Neural network based order parameter for phase transitions and its applications in high-entropy alloys
This work addresses a nontrivial problem in materials science for designing high-entropy alloys, offering a novel interpretable method that is incremental in applying existing ML techniques to a specific domain.
The paper tackles the challenge of identifying representative order parameters for complex systems like high-entropy alloys by introducing a 'VAE order parameter' based on Manhattan distance in a variational autoencoder latent space, demonstrating its application in alloy design with quantitative physical interpretations.
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 nontrivial, such as for high-entropy alloys. Given variational autoencoder's (VAE) strength of reducing high dimensional data into few principal components, here we coin a new concept of "VAE 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 the order parameter are quantitatively interpreted and demonstrated by multiple refractory high-entropy alloys. Assisted by it, a generally applicable alloy design concept is proposed by mimicking the nature mixing of elements. Our physically interpretable "VAE order parameter" lays the foundation for the understanding of and alloy design by chemical ordering.