Review of the Synergies Between Computational Modeling and Experimental Characterization of Materials Across Length Scales

With the increasing interplay between experimental and computational approaches at multiple length scales, new research directions are emerging in materials science and computational mechanics. Such cooperative interactions find many applications in the development, characterization and design of complex material systems. This manuscript provides a broad and comprehensive overview of recent trends where predictive modeling capabilities are developed in conjunction with experiments and advanced characterization to gain a greater insight into structure-properties relationships and study various physical phenomena and mechanisms. The focus of this review is on the intersections of multiscale materials experiments and modeling relevant to the materials mechanics community. After a general discussion on the perspective from various communities, the article focuses on the latest experimental and theoretical opportunities. Emphasis is given to the role of experiments in multiscale models, including insights into how computations can be used as discovery tools for materials engineering, rather than to "simply" support experimental work. This is illustrated by examples from several application areas on structural materials. This manuscript ends with a discussion on some problems and open scientific questions that are being explored in order to advance this relatively new field of research.Comment: 25 pages, 11 figures, review article accepted for publication in J. Mater. Sc

Atomic Representations of Local and Global Chemistry in Complex Alloys

The exceptional properties observed in complex concentrated alloys (CCAs) arise from the interplay between crystalline order and chemical disorder at the atomic scale, complicating a unique determination of properties. In contrast to conventional alloys, CCA properties emerge as distributions due to varying local chemical environments and the specific scale of measurement. Currently there are few ways to quantitatively define, track, and compare local alloy compositions (versus a global label, i.e. equiatomic) contained in a CCA. Molecular dynamics is used here to build descriptive metrics that connect a global alloy composition to the diverse local alloy compositions that define it. A machine-learned interatomic potential for MoNbTaTi is developed and we use these metrics to investigate how property distributions change with excursions in global-local composition space. Short-range order is examined through the lens of local chemistry for the equiatomic composition, demonstrating stark changes in vacancy formation energy with local chemistry evolution.Comment: Version 2: editing and figure improvements, overall content unchanged. 15 pages, 6 main figures, 1 supplemental figur

Deep material network via a quilting strategy: visualization for explainability and recursive training for improved accuracy

Abstract Recent developments integrating micromechanics and neural networks offer promising paths for rapid predictions of the response of heterogeneous materials with similar accuracy as direct numerical simulations. The deep material network is one such approaches, featuring a multi-layer network and micromechanics building blocks trained on anisotropic linear elastic properties. Once trained, the network acts as a reduced-order model, which can extrapolate the material’s behavior to more general constitutive laws, including nonlinear behaviors, without the need to be retrained. However, current training methods initialize network parameters randomly, incurring inevitable training and calibration errors. Here, we introduce a way to visualize the network parameters as an analogous unit cell and use this visualization to “quilt” patches of shallower networks to initialize deeper networks for a recursive training strategy. The result is an improvement in the accuracy and calibration performance of the network and an intuitive visual representation of the network for better explainability

Compositionally-Driven Formation Mechanism of Hierarchical Morphologies in Co-Deposited Immiscible Alloy Thin Films

Co-deposited, immiscible alloy systems form hierarchical microstructures under specific deposition conditions that accentuate the difference in constituent element mobility. The mechanism leading to the formation of these unique hierarchical morphologies during the deposition process is difficult to identify, since the characterization of these microstructures is typically carried out post-deposition. We employ phase-field modeling to study the evolution of microstructures during deposition combined with microscopy characterization of experimentally deposited thin films to reveal the origin of the formation mechanism of hierarchical morphologies in co-deposited, immiscible alloy thin films. Our results trace this back to the significant influence of a local compositional driving force that occurs near the surface of the growing thin film. We show that local variations in the concentration of the vapor phase near the surface, resulting in nuclei (i.e., a cluster of atoms) on the film’s surface with an inhomogeneous composition, can trigger the simultaneous evolution of multiple concentration modulations across multiple length scales, leading to hierarchical morphologies. We show that locally, the concentration must be above a certain threshold value in order to generate distinct hierarchical morphologies in a single domain

Linking simulated polycrystalline thin film microstructures to physical vapor deposition conditions

We present a generalized multi-phase-field model to predict the growth of polycrystalline thin films fabricated by physical vapor deposition. The model accounts for the explicit transport of atomic species to the substrate and the competing diffusion processes on the surface and in the bulk of the film leading to the formation of films with specific microstructures. We used magnetron sputtering conditions (pressure, voltage, working distance, substrate orientation) to calculate the energy and direction of the arriving atoms at the substrate using Monte Carlo simulations with the SiMTRA code. Our simulation results capture the dependence of the microstructure on deposition conditions, and delineate the relationship between process parameters and the formation of columnar microstructures and surface roughness characteristic of thin films. These simulation predictions are in agreement with transmission electron microscopy characterization of sputtered films. Through our systematic investigation of competing growth mechanisms, we provide insights into the complex relationships between deposition conditions and bulk and surface morphologies.We present a generalized multi-phase-field model to predict the growth of polycrystalline thin films fabricated by physical vapor deposition. The model accounts for the explicit transport of atomic species to the substrate and the competing diffusion processes on the surface and in the bulk of the film leading to the formation of films with specific microstructures. We used magnetron sputtering conditions (pressure, voltage, working distance, substrate orientation) to calculate the energy and direction of the arriving atoms at the substrate using Monte Carlo simulations with the SiMTRA code. Our simulation results capture the dependence of the microstructure on deposition conditions, and delineate the relationship between process parameters and the formation of columnar microstructures and surface roughness characteristic of thin films. These simulation predictions are in agreement with transmission electron microscopy characterization of sputtered films. Through our systematic investigation of competing growth mechanisms, we provide insights into the complex relationships between deposition conditions and bulk and surface morphologies.A