Abstract
Abstract
Highlights
Calphad-based thermodynamic models are routinely used to probe the phase stability in multicomponent systems
Computational efficiency and the ability to incorporate experimental measurements, atomistic simulations, and expert intuition in a semi-empirical fashion have led to the broad adoption of the Calphad approach, but it is only in recent years that serious attention has been paid to uncertainty quantification (UQ) of the model predictions
One would expect subsequent Markov Chain Monte Carlo (MCMC) optimization to be accelerated by the greater curvature of the augmented likelihood function and an uncertainty estimate closer to the CR bound to be achieved. It is promising for the future of Calphad sensitivity that this analysis was able to quantifiably reproduce the long-respected wisdom in the Calphad community that thermochemical measurements are the foundation of an accurate thermodynamic model, with the phase diagram playing a highly visible, yet merely supporting, role
Summary
Calphad-based thermodynamic models are routinely used to probe the phase stability in multicomponent systems. A sensitivity theory for Calphad was developed, and a closed-form expression for the log-likelihood gradient and Hessian of a multi-phase equilibrium measurement was presented. Clear definitions must be given to all observation types, including multi-phase equilibrium information, commonly referred to as “phase diagram data.” The development of a consistent framework for Calphad model sensitivity is necessary, for UQ, and for the rational reduction of uncertainty via new models and experiments.
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