Uncertainty Quantification of Atomistic (DFT and MD), Mesoscale (PFM) and Continuum (CALPHAD) Methods and the Impact on Thermodynamic Models of Metals: A Review
Gabriel Joshua, Noah Paulson, Thien Duong, Francesca Tavazza, Chandler Becker, Santanu Chaudhuri, Stan Marious
Design of improved metals relies on multi-scale computer simulations to provide thermodynamic properties when experiments are difficult to conduct. In particular, 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, with a focus on DFT and MD, in the context of information exchange with mesoscale and continuum 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. UQ information transfer among DFT, MD, PFM and CALPHAD is also discussed, including the impact on materials design.
JOM Journal of the Minerals Metals and Materials Society
, Paulson, N.
, Duong, T.
, Tavazza, F.
, Becker, C.
, Chaudhuri, S.
and Marious, S.
Uncertainty Quantification of Atomistic (DFT and MD), Mesoscale (PFM) and Continuum (CALPHAD) Methods and the Impact on Thermodynamic Models of Metals: A Review, JOM Journal of the Minerals Metals and Materials Society, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=930773
(Accessed June 17, 2021)