| Download | - View final version: Neural evolution structure generation: high entropy alloys (PDF, 5.9 MiB)
|
|---|
| DOI | Resolve DOI: https://doi.org/10.1063/5.0049000 |
|---|
| Author | Search for: Tetsassi Feugmo, Conrard Giresse1ORCID identifier: https://orcid.org/0000-0002-8992-4335; Search for: Ryczko, KevinORCID identifier: https://orcid.org/0000-0001-6933-3856; Search for: Anand, AbuORCID identifier: https://orcid.org/0000-0002-3041-4015; Search for: Singh, Chandra VeerORCID identifier: https://orcid.org/0000-0002-6644-0178; Search for: Tamblyn, Isaac2ORCID identifier: https://orcid.org/0000-0002-8146-6667 |
|---|
| Affiliation | - National Research Council of Canada. Energy, Mining and Environment
- National Research Council of Canada. Security and Disruptive Technologies
|
|---|
| Format | Text, Article |
|---|
| Abstract | We propose a neural evolution structure (NES) generation methodology combining artificial neural networks and evolutionary algorithms to generate high entropy alloy structures. Our inverse design approach is based on pair distribution functions and atomic properties and allows one to train a model on smaller unit cells and then generate a larger cell. With a speed-up factor of ∼1000 with respect to the special quasi-random structures (SQSs), the NESs dramatically reduce computational costs and time, making possible the generation of very large structures (over 40 000 atoms) in few hours. Additionally, unlike the SQSs, the same model can be used to generate multiple structures with the same fractional composition. |
|---|
| Publication date | 2021-07-22 |
|---|
| Publisher | American Institute of Physics |
|---|
| In | |
|---|
| Language | English |
|---|
| Peer reviewed | Yes |
|---|
| Export citation | Export as RIS |
|---|
| Report a correction | Report a correction (opens in a new tab) |
|---|
| Record identifier | 3b30e425-0723-4c7a-bc47-d313eaa92ca7 |
|---|
| Record created | 2021-08-19 |
|---|
| Record modified | 2021-08-19 |
|---|
