Thermodynamically Informed Priors for Uncertainty Propagation in First-Principles Statistical Mechanics

This work demonstrates how first-principles thermodynamic research within a Bayesian framework can quantify and propagate uncertainties to downstream thermodynamic calculations. To address the issue of Bayesian prior selection, knowledge of zero Kelvin ground states in the material system of interest is incorporated into the prior. The effectiveness of this framework is shown by creating a phase diagram for the FCC Zirconium Nitride system, including confidence intervals on phase boundary regions of interest.