CUED Publications database

Roadmap on multiscale materials modeling

Van Der Giessen, E and Schultz, PA and Bertin, N and Bulatov, VV and Cai, W and Csányi, G and Foiles, SM and Geers, MGD and González, C and Hütter, M and Kim, WK and Kochmann, DM and Llorca, J and Mattsson, AE and Rottler, J and Shluger, A and Sills, RB and Steinbach, I and Strachan, A and Tadmor, EB (2020) Roadmap on multiscale materials modeling. Modelling and Simulation in Materials Science and Engineering, 28. ISSN 0965-0393

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Abstract

© 2020 The Author(s). Published by IOP Publishing Ltd. 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.

Item Type: Article
Subjects: UNSPECIFIED
Divisions: Div C > Applied Mechanics
Depositing User: Cron Job
Date Deposited: 19 Jun 2020 21:20
Last Modified: 04 Mar 2021 04:11
DOI: 10.1088/1361-651X/ab7150