911 research outputs found
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
Modeling structural and electronic properties of nano-scale systems
Computergestütze Modellierung von organischen elektronsichen Materialien durch gezielte Untersuchung mikroskopischer Prozesse und Berechnung molekülspezifischer Materialparameter ermöglicht die effiziente Entwicklung langlebiger, effizienter Bauteile. In dieser Arbeit werden die strukturellen und elektronischen Eigenschaften organischer und metall-organischer Schichten untersucht, sowie effiziente Simulationsmethoden (weiter-)entwickelt
Roadmap on multiscale materials modeling
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
Ab initio atomistic thermodynamics and statistical mechanics of surface properties and functions
Previous and present "academic" research aiming at atomic scale understanding
is mainly concerned with the study of individual molecular processes possibly
underlying materials science applications. Appealing properties of an
individual process are then frequently discussed in terms of their direct
importance for the envisioned material function, or reciprocally, the function
of materials is somehow believed to be understandable by essentially one
prominent elementary process only. What is often overlooked in this approach is
that in macroscopic systems of technological relevance typically a large number
of distinct atomic scale processes take place. Which of them are decisive for
observable system properties and functions is then not only determined by the
detailed individual properties of each process alone, but in many, if not most
cases also the interplay of all processes, i.e. how they act together, plays a
crucial role. For a "predictive materials science modeling with microscopic
understanding", a description that treats the statistical interplay of a large
number of microscopically well-described elementary processes must therefore be
applied. Modern electronic structure theory methods such as DFT have become a
standard tool for the accurate description of individual molecular processes.
Here, we discuss the present status of emerging methodologies which attempt to
achieve a (hopefully seamless) match of DFT with concepts from statistical
mechanics or thermodynamics, in order to also address the interplay of the
various molecular processes. The new quality of, and the novel insights that
can be gained by, such techniques is illustrated by how they allow the
description of crystal surfaces in contact with realistic gas-phase
environments.Comment: 24 pages including 17 figures, related publications can be found at
http://www.fhi-berlin.mpg.de/th/paper.htm
Surface Engineering for Phase Change Heat Transfer: A Review
Among numerous challenges to meet the rising global energy demand in a
sustainable manner, improving phase change heat transfer has been at the
forefront of engineering research for decades. The high heat transfer rates
associated with phase change heat transfer are essential to energy and industry
applications; but phase change is also inherently associated with poor
thermodynamic efficiencies at low heat flux, and violent instabilities at high
heat flux. Engineers have tried since the 1930's to fabricate solid surfaces
that improve phase change heat transfer. The development of micro and
nanotechnologies has made feasible the high-resolution control of surface
texture and chemistry over length scales ranging from molecular levels to
centimeters. This paper reviews the fabrication techniques available for
metallic and silicon-based surfaces, considering sintered and polymeric
coatings. The influence of such surfaces in multiphase processes of high
practical interest, e.g., boiling, condensation, freezing, and the associated
physical phenomena are reviewed. The case is made that while engineers are in
principle able to manufacture surfaces with optimum nucleation or thermofluid
transport characteristics, more theoretical and experimental efforts are needed
to guide the design and cost-effective fabrication of surfaces that not only
satisfy the existing technological needs, but also catalyze new discoveries
Microstructural modeling and computational homogenization of the physically linear and nonlinear constitutive behavior of micro-heterogeneous materials
Engineering materials show a pronounced heterogeneity on a smaller scale that influences the macroscopic constitutive behavior. Algorithms for the periodic discretization of microstructures are presented. These are used within the Nonuniform Transformation Field Analysis (NTFA) which is an order reduction based nonlinear homogenization method with micro-mechanical background. Theoretical and numerical aspects of the method are discussed and its computational efficiency is validated
Reducing time to discovery : materials and molecular modeling, imaging, informatics, and integration
This work was supported by the KAIST-funded Global Singularity Research Program for 2019 and 2020. J.C.A. acknowledges support from the National Science Foundation under Grant TRIPODS + X:RES-1839234 and the Nano/Human Interfaces Presidential Initiative. S.V.K.’s effort was supported by the U.S. Department of Energy (DOE), Office of Science, Basic Energy Sciences (BES), Materials Sciences and Engineering Division and was performed at the Oak Ridge National Laboratory’s Center for Nanophase Materials Sciences (CNMS), a U.S. Department of Energy, Office of Science User Facility.Multiscale and multimodal imaging of material structures and properties provides solid ground on which materials theory and design can flourish. Recently, KAIST announced 10 flagship research fields, which include KAIST Materials Revolution: Materials and Molecular Modeling, Imaging, Informatics and Integration (M3I3). The M3I3 initiative aims to reduce the time for the discovery, design and development of materials based on elucidating multiscale processing-structure-property relationship and materials hierarchy, which are to be quantified and understood through a combination of machine learning and scientific insights. In this review, we begin by introducing recent progress on related initiatives around the globe, such as the Materials Genome Initiative (U.S.), Materials Informatics (U.S.), the Materials Project (U.S.), the Open Quantum Materials Database (U.S.), Materials Research by Information Integration Initiative (Japan), Novel Materials Discovery (E.U.), the NOMAD repository (E.U.), Materials Scientific Data Sharing Network (China), Vom Materials Zur Innovation (Germany), and Creative Materials Discovery (Korea), and discuss the role of multiscale materials and molecular imaging combined with machine learning in realizing the vision of M3I3. Specifically, microscopies using photons, electrons, and physical probes will be revisited with a focus on the multiscale structural hierarchy, as well as structure-property relationships. Additionally, data mining from the literature combined with machine learning will be shown to be more efficient in finding the future direction of materials structures with improved properties than the classical approach. Examples of materials for applications in energy and information will be reviewed and discussed. A case study on the development of a Ni-Co-Mn cathode materials illustrates M3I3's approach to creating libraries of multiscale structure-property-processing relationships. We end with a future outlook toward recent developments in the field of M3I3.Peer reviewe
Microstructure generation and micromechanical modeling of sheet molding compound composites
We introduce an algorithm that allows for a fast generation of SMC composite microstructures. An exact closure approximation and a quasi-random orientation sampling ensure high fidelity. Furthermore, we present a modular framework for anisotropic damage evolution. Our concept of extraction tensors and damage-hardening functions enables the description of complex damage-degradation. In addition, we propose a holistic multiscale approach for constructing anisotropic failure criteria
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