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 solid 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 first-principles phase diagram  using ESPEI , a sequential learning approach is taken to systematically add thermodynamic and phase boundary data in regions of highest uncertainty. This simulates how the first-principles only phase diagram can most efficiently guide experimental investigation towards a thorough understanding of a materials system. This convergence towards a target phase diagram will be quantified and compared to the chronological introduction of experimental data.
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