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

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 molec...

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Bibliographic Details
Published in:JOM (1989) Vol. 73; no. 1; pp. 149 - 163
Main Authors: Gabriel, Joshua J., Paulson, Noah H., Duong, Thien C., Tavazza, Francesca, Becker, Chandler A., Chaudhuri, Santanu, Stan, Marius
Format: Journal Article
Language:English
Published: New York Springer US 01.01.2021
Springer Nature B.V
Springer
Subjects:
Accuracy
Aluminum
Approximation
Augmenting Physics-based Models in ICME with Machine Learning and Uncertainty Quantification
Bayesian analysis
Chemistry/Food Science
Continuum modeling
Density functional theory
Earth Sciences
Engineering
Environment
Epistemology
Materials engineering
MATERIALS SCIENCE
Mesoscale phenomena
Molecular dynamics
Phase diagrams
Phase transitions
Physics
Probability distribution
Propagation
Simulation
Statistical inference
Thermodynamic models
Thermodynamic properties
Uncertainty
ISSN:1047-4838, 1543-1851
Online Access:Get full text
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Summary: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.
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USDOE Office of Science (SC)
AC02-06CH11357; 70NANB14H012; 70NANB19H005; PRJ1007310
USDOE Laboratory Directed Research and Development (LDRD) Program
USDOE Advanced Research Projects Agency - Energy (ARPA-E)
National Institute of Standards and Technology (NIST)
ISSN:1047-4838
1543-1851
DOI:10.1007/s11837-020-04436-6