Uncertainty Quantification in Atomistic Modeling of Metals and Its Effect on Mesoscale and Continuum Modeling: A Review.

Autor: Gabriel JJ; Applied Materials Division, Argonne National Laboratory, Lemont, IL 60439, USA., Paulson NH; Applied Materials Division, Argonne National Laboratory, Lemont, IL 60439, USA., Duong TC; Energy and Global Security, Argonne National Laboratory, Lemont, IL 60439, USA., Tavazza F; Material Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, MD 20899, USA., Becker CA; Office of Data and Informatics, Material Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, MD 20899, USA., Chaudhuri S; Manufacturing Science and Engineering, Energy and Global Security, Argonne National Laboratory, Lemont, IL 60439, USA.; Civil, Materials, and Environmental Engineering, University of Illinois at Chicago, Chicago, IL 60607, USA., Stan M; Applied Materials Division, Argonne National Laboratory, Lemont, IL 60439, USA.
Jazyk: angličtina
Zdroj: JOM (Warrendale, Pa. : 1989) [JOM (1989)] 2021; Vol. 73.
DOI: 10.1007/s11837-020-04436-6
Abstrakt: The design of next-generation alloys through the integrated computational materials engineering (ICME) approach relies on multiscale 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 that mitigate this gap have emerged. These advances are reviewed in the context of thermodynamic modeling and information exchange with mesoscale methods such as the 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.
Competing Interests: CONFLICT OF INTEREST The authors declare no competing financial interests in the writing of this manuscript.
Databáze: MEDLINE
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