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The recent decades have seen various attempts at accelerating the process of developing materials targeted towards specific applications.
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Mixed atomistic-continuum models of material behavior: The art of transcending atomistics and informing continua
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Microstructure of martensite: why it forms and how it gives rise to the shape-memory effect
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A model problem concerning recoverable strains of shape-memory polycrystals
K. Bhattacharya and P. Suquet · 2005
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Multiscale modelling of defect kinetics in irradiated iron
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Failure surfaces for finitely strained two-phase periodic solids under general in-plane loading
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A unified architecture for natural language processing: Deep neural networks with multitask learning
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Multiscale methods: averaging and homogenization
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Multiscale Methods: Bridging the Scales in Science and Engineering
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Gmsh: A 3-d finite element mesh generator with built-in pre-and post-processing facilities
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Electron backscatter diffraction in materials science
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Data-intensive science: The terapixel and modisazure projects
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Multiscale Methods in Computational Mechanics
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Correlation analysis of non-probabilistic convex model and corresponding structural reliability technique
C. Jiang, X. Han, G. Lu, J. Liu, Z. Zhang, and Y. Bai · 2011
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Ultralight metallic microlattices
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Rigorous uncertainty quantification without integral testing
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Grain-scale experimental investigation of localised deformation in sand: a discrete particle tracking approach
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A polarization-based FFT iterative scheme for computing the effective properties of elastic composites with arbitrary contrast
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A review of additive manufacturing
K. V. Wong and A. Hernandez · 2012
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The Materials Project: A materials genome approach to accelerating materials innovation
A. Jain, S. P. Ong, G. Hautier, W. Chen, W. D. Richards, S. Dacek, S. Cholia, D. Gunter, D. Skinner, and G. Ceder · 2013
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Structural reliability analysis using non-probabilistic convex model
C. Jiang, R. Bi, G. Lu, and X. Han · 2013
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Big data, data science, and analytics: The opportunity and challenge for is research
R. Agarwal and V. Dhar · 2014
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Validation of a numerical method based on fast Fourier transforms for heterogeneous thermoelastic materials by comparison with analytical solutions
B. Anglin, R. Lebensohn, and A. Rollett · 2014
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Analytics in a big data world : the essential guide to data science and its applications
B. Baesens · 2014
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A high-performance computational framework for fast crystal plasticity simulations
M. Knezevic and D. J. Savage · 2014
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Strong, lightweight, and recoverable three-dimensional ceramic nanolattices
L. R. Meza, S. Das, and J. R. Greer · 2014
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Three orders of magnitude improved efficiency with high-performance spectral crystal plasticity on gpu platforms
B. Mihaila, M. Knezevic, and A. Cardenas · 2014
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Comparison of three accelerated FFT-based schemes for computing the mechanical response of composite materials
H. Moulinec and F. Silva · 2014
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Failure mechanism maps for ultra-high molecular weight polyethylene fibre composite beams impacted by blunt projectiles
B. Liu, H. Wadley, and V. Deshpande · 2019
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A deep material network for multiscale topology learning and accelerated nonlinear modeling of heterogeneous materials
Z. Liu, C. Wu, and M. Koishi · 2019
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Discovery of new materials using combinatorial synthesis and high-throughput characterization of thin-film materials libraries combined with computational methods
A. Ludwig · 2019
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Deep learning predicts path-dependent plasticity
M. Mozaffar, R. Bostanabad, W. Chen, K. Ehmann, J. Cao, and M. A. Bessa · 2019
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High strength metallic wood from nanostructured nickel inverse opal materials
J. H. Pikul, S. Özerinç, B. Liu, R. Zhang, P. V. Braun, V. S. Deshpande, and W. P. King · 2019
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M. O’Masta, V. Deshpande, and H. Wadley · 2014
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An FFT-based Galerkin method for homogenization of periodic media
J. Vondřejc, J. Zeman, and I. Marek · 2014
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Variational Methods for the Numerical Solution of Nonlinear Elliptic Problems
R. Glowinski · 2015
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Materials data science: current status and future outlook
S. Kalidindi and M. De Graef · 2015
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Microstructures to control elasticity in 3d printing
C. Schumacher, B. Bickel, J. Rys, S. Marschner, C. Daraio, and M. Gross · 2015
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A science data gateway for environmental management
D. A. Agarwal, B. Faybishenko, V. L. Freedman, H. Krishnan, G. Kushner, C. Lansing, E. Porter, A. Romosan, A. Shoshani, H. Wainwright, A. Weidmer, and K. S. Wu · 2016
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Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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Atomistic modeling and analysis of hydride phase transformation in palladium nanoparticles
X. Sun, M. Ariza, M. Ortiz, and K. Wang · 2019
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Analyzing machine learning models to accelerate generation of fundamental materials insights
M. Umehara, H. S. Stein, D. Guevarra, P. F. Newhouse, D. A. Boyd, and J. M. Gregoire · 2019
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Hybrid neural network potential for multilayer graphene
M. Wen and E. B. Tadmor · 2019
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A machine-learning-enhanced hierarchical multiscale method for bridging from molecular dynamics to continua
S. Xiao, R. Hu, Z. Li, S. Attarian, K.-M. Björk, and A. Lendasse · 2019
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Dakota, a multilevel parallel object-oriented framework for design optimization, parameter estimation, uncertainty quantification, and sensitivity analysis: version 6.12 user’s manual
B. M. Adams, W. J. Bohnhoff, K. R. Dalbey, M. S. Ebeida, J. P. Eddy, M. S. Eldred, R. W. Hooper, P. D. Hough, K. T. Hu, J. D. Jakeman, M. Khalil, K. A. Maupin, J. A. Monschke, E. M. Ridgway, A. A. Rushdi, D. T. Seidl, J. A. Stephens, L. P. Swiler, and J. G. Winokur · 2020
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A fast Fourier transform-based mesoscale field dislocation mechanics study of grain size effects and reversible plasticity in polycrystals
S. Berbenni, V. Taupin, and R. A. Lebensohn · 2020
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Model reduction and neural networks for parametric PDEs
K. Bhattacharya, B. Hosseini, N. Kovachki, and A. M. Stuart · 2020
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Model Reduction and Neural Networks for Parametric PDEs
K. Bhattacharya, B. Hosseini, N. B. Kovachki, and A. M. Stuart · 2020
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Data-driven fracture mechanics
P. Carrara, L. De Lorenzis, L. Stainier, and M. Ortiz · 2020
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A machine learning based intramolecular potential for a flexible organic molecule
D. J. Cole, L. Mones, and G. Csányi · 2020
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Data-driven finite elasticity
S. Conti, S. Müller, and M. Ortiz · 2020
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A dictionary indexing approach for EBSD
M. De Graef · 2020
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Neural network model describing the temperature-and rate-dependent stress-strain response of polypropylene
B. Jordan, M. B. Gorji, and D. Mohr · 2020
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Multipole graph neural operator for parametric partial differential equations
Z. Li, N. Kovachki, K. Azizzadenesheli, B. Liu, K. Bhattacharya, A. Stuart, and A. Anandkumar · 2020
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Machine learning for metallurgy i. a neural-network potential for al-cu
D. Marchand, A. Jain, A. Glensk, and W. Curtin · 2020
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Substitution of subspace collections with nonorthogonal subspaces to accelerate Fast Fourier Transform methods applied to conducting composites
G. W. Milton · 2020
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An FFT-based method for computing weighted minimal surfaces in microstructures with applications to the computational homogenization of brittle fracture
M. Schneider · 2020
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Rigorous uncertainty quantification and design with uncertain material models
X. Sun, T. Kirchdoerfer, and M. Ortiz · 2020
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Goal-oriented materials-by-design
X. Sun, B. Liu, K. Bhattacharya, and M. Ortiz · 2020
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Roadmap on multiscale materials modeling
E. Van Der Giessen, P. A. Schultz, N. Bertin, V. V. Bulatov, W. Cai, G. Csányi, S. M. Foiles, M. G. Geers, C. González, M. Hütter, et al · 2020
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Out-of-plane compressive response of additively manufactured cross-ply composites
R. Yogeshvaran, B. Liu, F. Farukh, and K. Kandan · 2020
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Data-driven multiscale modeling in mechanics
K. Karapiperis, L. Stainier, M. Ortiz, and J. Andrade · 2021
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Fourier neural operator for parametric partial differential equations
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A learning-based multiscale method and its application to inelastic impact problems
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Hierarchical multiscale quantification of material uncertainty
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Finite element solver for data-driven finite strain elasticity
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