Special Issue: Predictive multiscale materials modeling

Katsoulakis, Markos A.; Zabaras, Nicholas

doi:10.1016/j.jcp.2017.02.045

Cited by 2 publications

(1 citation statement)

References 11 publications

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“…The second broad challenge arises in the context of multi-scale materials modeling [24]. Conceptually, information obtained from simulation(s) at smaller length/time scales are synthesized and used to inform models at higher scales [25][26][27].…”

Section: Uq For Materialsmentioning

confidence: 99%

Roadmap on multiscale materials modeling

Giessen

Schultz

Bertin

et al. 2020

Modelling Simul. Mater. Sci. Eng.

137

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Modeling and simulation is transforming modern materials science, becoming an important tool for the discovery of new materials and material phenomena, for gaining insight into the processes that govern materials behavior, and, increasingly, for quantitative predictions that can be used as part of a design tool in full partnership with experimental synthesis and characterization. Modeling and simulation is the essential bridge from good science to good engineering, spanning from fundamental understanding of materials behavior to deliberate design of new materials technologies leveraging new properties and processes. This Roadmap presents a broad overview of the extensive impact computational modeling has had in materials science in the past few decades, and offers focused perspectives on where the path forward lies as this rapidly expanding field evolves to meet the challenges of the next few decades. The Roadmap offers perspectives on advances within disciplines as diverse as phase field methods to model mesoscale behavior and molecular dynamics methods to deduce the fundamental atomic-scale dynamical processes governing materials response, to the challenges involved in the interdisciplinary research that tackles complex materials problems where the governing phenomena span different scales of materials behavior requiring multiscale approaches. The shift from understanding fundamental materials behavior to development of quantitative approaches to explain and predict experimental observations requires advances in the methods and practice in simulations for reproducibility and reliability, and interacting with a computational ecosystem that integrates new theory development, innovative applications, and an increasingly integrated software and computational infrastructure that takes advantage of the increasingly powerful computational methods and computing hardware.

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Section: Uq For Materialsmentioning

confidence: 99%

Roadmap on multiscale materials modeling

Giessen

Schultz

Bertin

et al. 2020

Modelling Simul. Mater. Sci. Eng.

137

View full text Add to dashboard Cite

show abstract

A stable cardinality distance for topological classification

Maroulas

Micucci

Spannaus

2019

Adv Data Anal Classif

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A. This work incorporates topological features via persistence diagrams to classify point cloud data arising from materials science. Persistence diagrams are multisets summarizing the connectedness and holes of given data. A new distance on the space of persistence diagrams generates relevant input features for a classification algorithm for materials science data. This distance measures the similarity of persistence diagrams using the cost of matching points and a regularization term corresponding to cardinality differences between diagrams. Establishing stability properties of this distance provides theoretical justification for the use of the distance in comparisons of such diagrams. The classification scheme succeeds in determining the crystal structure of materials on noisy and sparse data retrieved from synthetic atom probe tomography experiments.

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Special Issue: Predictive multiscale materials modeling

Cited by 2 publications

References 11 publications

Roadmap on multiscale materials modeling

Roadmap on multiscale materials modeling

A stable cardinality distance for topological classification

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