Speaker
Description
Accurate simulation of finite-temperature properties of metallic alloys is often a challenge for computer-aided materials design, mostly performed with density-functional theory (DFT) calculations.
Not only it requires time-consuming molecular dynamics (MD) simulations to capture finite-temperature effects, but also questions like phase stability at different temperatures arise and become important, especially in the case of high-entropy alloys.
In addition, extensive study of a range of materials is often infeasible due to much time consumed by DFT calculations.
In the present work we tackle the challenge of investigating the relation between elastic properties and dynamical instability of bcc-phase in TiZrHfTa$_x$ alloys in a wide temperature range with varying Ta concentration.
We achieve a great performance boost while not sacrificing much accuracy (compared to DFT) by using both accurate interpolation of ab-initio data and intelligent learning principle (theo so-called active learning), allowing for extracting maximum information from the least possible number of DFT calculations.
For bcc-$\omega$ phases distinction we use recently proposed scheme based on analysis of atomic displacements along transformation directions during finite-temperature MD.
As a result a correlation between $C_{44}$ elastic constant and $\omega$-formation tendency has been revealed depending on the temperature and Ta concentration.
The research involves using such software as VASP (for ab-initio energies/forces), LAMMPS (for MD simulations) and MLIP (for interpolation of DFT data).
The framework proposed in the present work is essentially generic and can be used for different high-entropy alloys as well.
| Speaker Country | Germany |
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