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.
| Original language | English |
|---|---|
| Pages (from-to) | 686-693 |
| Number of pages | 8 |
| Journal | Nature Computational Science |
| Volume | 1 |
| Issue number | 10 |
| DOIs | |
| State | Published - Oct 2021 |
Funding
This research was sponsored by and used resources of the Oak Ridge Leadership Computing Facility (OLCF), which is a US Department of Energy (DOE) Office of Science User Facility at the Oak Ridge National Laboratory supported by the US DOE under contract no. DE-AC05-00OR22725. This work was also performed in support of the US DOE’s Fossil Energy Crosscutting Technology Research Program, and in part by an appointment to the US DOE Postgraduate Research Program at the National Energy Technology Laboratory (NETL) administered by the Oak Ridge Institute for Science and Education. Neither the United States Government nor any agency thereof, nor any of its employees, nor the support contractor, nor any of their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof.
| Funders | Funder number |
|---|---|
| U.S. Department of Energy | DE-AC05-00OR22725 |
| Office of Science | |
| Oak Ridge Institute for Science and Education | |
| National Energy Technology Laboratory |