| About this Abstract |
| Meeting |
2020 TMS Annual Meeting & Exhibition
|
| Symposium
|
ICME Gap Analysis in Materials Informatics: Databases, Machine Learning, and Data-Driven Design
|
| Presentation Title |
Reduction of Uncertainty in a First-principles-based CALPHAD-type Phase Diagram via Sequential Learning of Phase Equilibrium Data |
| Author(s) |
Theresa Davey, Brandon J. Bocklund, Zi-Kui Liu, Ying Chen |
| On-Site Speaker (Planned) |
Theresa Davey |
| Abstract Scope |
Phase diagrams are a fundamental tool in materials design, but thorough experimental determination is challenging, expensive, and time consuming. Phase diagrams calculated entirely from first-principles may reduce time and expense, providing information at the prediction stage. Our previous work demonstrated a methodology to obtain a first-principles-only CALPHAD-type phase diagram reproducing all major features, with little or no prior knowledge of the system [1]. This can guide reduced experiments needed for database validation.
Considering the quantified uncertainty of the phase diagram [2] using ESPEI [3], a sequential learning approach is taken to systematically add data in regions of highest uncertainty. This models how the first-principles only phase diagram could help select experimental parameters, and how each experiment affects the phase diagram.
[1] T. Davey et al., CALPHAD XLVIII, June 2019.
[2] N. Paulson et al., Acta Mater. 174 (2019) 9–15.
[3] B. Bocklund et al., MRS Commun. (2019) 1-10. |
| Proceedings Inclusion? |
Planned: Supplemental Proceedings volume |