Reduction of uncertainty in a first-principles-based CALPHAD-type phase diagram via sequential learning of phase equilibrium data

Theresa Davey1, Brandon J. Bocklund2, Zi-Kui Liu2, Ying Chen1

1. School of Engineering, Tohoku University, Japan
2. Department of Materials Science and Engineering, Pennsylvania State University, USA

TMS 2020, San Diego, CA, USA

Contributed oral presentation in ICME Gap Analysis in Materials Informatics: Databases, Machine Learning, and Data-Driven Design

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.