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In this work, an assessment of the CALPHAD method trained on only densityfunctional theory (DFT) data is performed for the Li-Si binary system, as acase study. Using a parameter sampling approach based on the Bayesian ErrorEstimation Functional (BEEF-vdW) exchange-correlation potential. By usingbuilt-in ensemble of functionals from BEEF-vdW, the uncertainties of the GibbsFree Energy fitting parameters are obtained and can be propagated to theresulting phase diagram. To find the best fitting form of the CALPHAD model, weimplement a model selection step using the Bayesian information criterion(BIC). Applying the best selected CALPHAD model from the DFT calculation, toother sampled BEEF functionals, an ensemble of CALPHAD models is generatedleading to an ensemble of phase diagram predictions. The resulting phasediagrams are then compiled into a single-phase diagram representing the mostprobable phase predicted as well as a quantitative metric of confidence for theprediction. This treatment of uncertainty resulting from DFT provides arigorous way to ensure the correlated errors of DFT is accounted for in theestimation of uncertainty. From the phase diagram, we have determinedintercalation voltages for lithiated silicon. In combination, we can generate abetter understanding of the phase transitions and voltage profile to make amore analysis-informed prediction for experiments and the performance ofSi-anodes within batteries.

Uncertainty Quantification of First Principles Computational Phase Diagram Predictions of Li-Si System Via Bayesian Sampling