| About this Abstract |
| Meeting |
2022 TMS Annual Meeting & Exhibition
|
| Symposium
|
Algorithm Development in Materials Science and Engineering
|
| Presentation Title |
Predicting Temperature-dependent Oxide Redox Reactions with Machine-learning Augmented First-principles Calculations |
| Author(s) |
José Antonio Garrido Torres, Vahe Gharakhanyan, Tobias Hoffmann Eegholm, Nongnuch Artrith, Alexander Urban |
| On-Site Speaker (Planned) |
Alexander Urban |
| Abstract Scope |
The experimental characterization of high-temperature redox chemistry is challenging and requires specialized equipment. CALPHAD simulations can be an alternative but require assessed phase diagrams for the system of interest. First-principles calculations, on the other hand, can provide robust estimates of redox potentials at zero Kelvin without experimental input, but simulating redox reactions at high temperatures is computationally demanding and often too approximate. Here, we discuss initial progress towards the efficient computational prediction of high-temperature redox chemistry using machine-learning augmented first-principles calculations. We show that a combination of results from zero-Kelvin density-functional theory (DFT) calculations and a machine-learning model trained on temperature-dependent reaction free energies allows predicting reduction temperatures of metal oxides. An initial application to crystalline binary and ternary oxides demonstrates that the temperature dependence of the free energy can be cross-learned, removing the limitation to compounds that have previously been thermodynamically assessed. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Pyrometallurgy, Machine Learning, Computational Materials Science & Engineering |