Novel method for automatic search for stable ordered phases in multicomponent systems

Machine learning of phase diagrams

“…12 At the heart of a modern PD calculation is the optimization (fitting) of the parameters associated to the Gibbs free energy function that describes a material phase. This optimization is one of the most widely active CALPHAD research areas, as new chemistries become the focus of emerging technological applications, including for high-entropy materials, [13][14][15][16] and in uncertainty quantification. [17][18][19][20][21][22][23] Traditionally, the PD construction process combines experimental (e.g., calorimetric, electrochemical, vapor pressures, and phase boundary measurements) and most recently atomistic calculations 24 to enable the prediction of a PD by starting from the periodic table, while simultaneously enabling a deeper understanding of the effect of bonding and solid-state physics on the properties and coexistence of phases.…”

“…At the heart of a modern PD calculation is the optimization (fitting) of the parameters associated to the Gibbs free energy function that describes a material phase. This optimization is one of the most widely active CALPHAD research areas, as new chemistries become the focus of emerging technological applications, including for high-entropy materials, 13–16 and in uncertainty quantification. 17–23 Traditionally, the PD construction process combines experimental ( e.g.…”