  The design of next-generation alloys through the Integrated Computational
Materials Engineering (ICME) approach relies on multi-scale computer
simulations to provide thermodynamic properties when experiments are difficult
to conduct. Atomistic methods such as Density Functional Theory (DFT) and
Molecular Dynamics (MD) have been successful in predicting properties of never
before studied compounds or phases. However, uncertainty quantification (UQ) of
DFT and MD results is rarely reported due to computational and UQ methodology
challenges. Over the past decade, studies have emerged that mitigate this gap.
These advances are reviewed in the context of thermodynamic modeling and
information exchange with mesoscale methods such as Phase Field Method (PFM)
and Calculation of Phase Diagrams (CALPHAD). The importance of UQ is
illustrated using properties of metals, with aluminum as an example, and
highlighting deterministic, frequentist and Bayesian methodologies. Challenges
facing routine uncertainty quantification and an outlook on addressing them are
also presented.
