Coupling in situ experiments and modeling – Opportunities for data fusion, machine learning, and discovery of emergent behavior. Issue 1 (February 2020)

• Record Type: Journal Article
• Authors: Sangid, Michael D.
• Is Part Of: Current opinion in solid state & materials science. Volume 24:Issue 1(2020)
• Publication Date: 2020-02
• Subjects: Microstructure modeling -- Molecular dynamics -- In situ transmission electron microscopy -- Dislocation dynamics -- Crystal plasticity -- Digital image correlation -- In situ high energy X-ray diffraction microscopy -- Artificial intelligence -- Data driven approaches -- Material informatics; Materials science -- Periodicals; Solid state physics -- Periodicals; 620.11
• DOI: 10.1016/j.cossms.2019.100797
• Languages: English
• ISSNs: 1359-0286

Abstract

Highlights: Review of multiscale approaches to couple in situ experiments and modeling. Discusses opportunities for direct model calibration, verification, and validation. Data driven opportunities with machine learning can provide emergent behavior. Abstract: This paper reviews recent studies, that not only includes both experiments and modeling components, but celebrates a close coupling between these techniques, in order to provide insights into the plasticity and failure of polycrystalline metals. Examples are provided of studies across multiple-scales, including, but not limited to, density functional theory combined with atom probe tomography, molecular dynamics combined with in situ transmission electron miscopy, discrete dislocation dynamics combined with nanopillars experiments, crystal plasticity combined with digital image correlation, and crystal plasticity combined with in situ high energy X-ray diffraction. The close synergy between in situ experiments and modeling provides new opportunities for model calibration, verification, and validation, by providing direct means of comparison, thus removing aspects of epistemic uncertainty in the approach. Further, data fusion between in situ experimental and model-based data, along with data driven approaches, provides a paradigm shift for determining the emergent behavior of deformation and failure, which is the foundation that underpins the mechanical behavior of polycrystalline materials.