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 . This can guide reduced experiments needed for database validation.
Considering the quantified uncertainty of the phase diagram  using ESPEI , 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.
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