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

  • Augmenting Physics-based Models in ICME with Machine Learning and Uncertainty Quantification
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Abstract

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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Acknowledgements

J.J.G., N.H.P., and M.S. gratefully acknowledge financial support from awards 70NANB14H012 and 70NANB19H005 from US Department of Commerce, National Institute of Standards and Technology as part of the Center for Hierarchical Materials Design (CHiMaD) in the Northwestern-Argonne Institute of Science and Engineering, and the Laboratory Directed Research and Development (LDRD) funding from Argonne National Laboratory, provided by the Director, Office of Science, of the US Department of Energy under Contract No. DE-AC02-06CH11357. T.C.D. and S.C. thank the ARPA-E for its support under Contract Number PRJ1007310.

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  1. Applied Materials Division, Argonne National Laboratory, Lemont, IL, 60439, USA

    Joshua J. Gabriel, Noah H. Paulson & Marius Stan

  2. Energy and Global Security, Argonne National Laboratory, Lemont, IL, 60439, USA

    Thien C. Duong

  3. Material Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, MD, 20899, USA

    Francesca Tavazza

  4. Office of Data and Informatics, Material Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, MD, 20899, USA

    Chandler A. Becker

  5. Manufacturing Science and Engineering, Energy and Global Security, Argonne National Laboratory, Lemont, IL, 60439, USA

    Santanu Chaudhuri

  6. Civil, Materials, and Environmental Engineering, University of Illinois at Chicago, Chicago, IL, 60607, USA

    Santanu Chaudhuri

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Gabriel, J.J., Paulson, N.H., Duong, T.C. et al. Uncertainty Quantification in Atomistic Modeling of Metals and Its Effect on Mesoscale and Continuum Modeling: A Review. JOM 73, 149–163 (2021). https://doi.org/10.1007/s11837-020-04436-6

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