Accelerating ab initio melting property calculations with machine learning: application to the high entropy alloy TaVCrW
Li-Fang Zhu, Fritz Körmann, Qing Chen, Malin Selleby, Jörg Neugebauer, Blazej Grabowski · 2024
Read Accelerating ab initio melting property calculations with machine learning: application to the high entropy alloy TaVCrW by Li-Fang Zhu, Fritz Körmann, Qing Chen, Malin Selleby, Jörg Neugebauer, Blazej Grabowski (2024) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-11606917.txt.
Description
Li-Fang Zhu and co-authors speed melting point theory with machine learning in a 2024 study. Refractory alloys melt so hot that measuring is brutal, yet design needs melting data. The gold standard theory route integrates costly atomistic simulations. Their method keeps the quantum accuracy but trains a learning model to carry the heavy parts.
The test case is a four metal high entropy alloy of tantalum, vanadium, chromium, and tungsten. Faster melting predictions open high throughput screening: many candidate alloys can be ranked before any furnace is lit. The approach generalizes the free energy method that materials theory trusts, at a fraction of the computing bill.
Catalog record
| CREATOR | Li-Fang Zhu, Fritz Körmann, Qing Chen, Malin Selleby, Jörg Neugebauer, Blazej Grabowski |
|---|---|
| YEAR | 2024 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 66 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |