Material structure-property linkages using three-dimensional convolutional neural networks

[1]  E. Kröner.  Bounds for effective elastic moduli of disordered materials , 1977 .

[2]  James G. Berryman,et al.  Measurement of spatial correlation functions using image processing techniques , 1985 .

[3]  S. Torquato,et al.  Lineal-path function for random heterogeneous materials. , 1992, Physical review. A, Atomic, molecular, and optical physics.

[4]  O. Levy,et al.  Weakly nonlinear conductivity of random composites: A series expansion approach , 1996 .

[5]  G. B. Olson,et al.  Computational Design of Hierarchically Structured Materials , 1997 .

[6]  Yoshua Bengio,et al.  Gradient-based learning applied to document recognition , 1998, Proc. IEEE.

[7]  J. Michel,et al.  Effective properties of composite materials with periodic microstructure : a computational approach , 1999 .

[8]  Andrew W. Moore,et al.  'N-Body' Problems in Statistical Learning , 2000, NIPS.

[9]  James G. Berryman,et al.  Elastic moduli of a material containing composite inclusions: effective medium theory and finite element computations , 2001 .

[10]  Yijun Liu,et al.  Evaluations of the effective material properties of carbon nanotube-based composites using a nanoscale representative volume element , 2003 .

[11]  Dimitris C. Lagoudas,et al.  Micromechanical analysis of the effective elastic properties of carbon nanotube reinforced composites , 2006 .

[12]  H. Garmestani,et al.  Microstructure design of a two phase composite using two-point correlation functions , 2004 .

[13]  Mingzhong Li,et al.  Determination of non-spherical particle size distribution from chord length measurements. Part 1: Theoretical analysis , 2005 .

[14]  Nasser M. Nasrabadi,et al.  Pattern Recognition and Machine Learning , 2006, Technometrics.

[15]  P. D. Chinh.  Three-point interpolation approximation for the macroscopic properties of isotropic two-component materials , 2007 .

[16]  D. Fullwood,et al.  Microstructure reconstructions from 2-point statistics using phase-recovery algorithms , 2008 .

[17]  David T. Fullwood,et al.  A strong contrast homogenization formulation for multi-phase anisotropic materials , 2008 .

[18]  D. Fullwood,et al.  Delineation of the space of 2-point correlations in a composite material system , 2008 .

[19]  David L. McDowell,et al.  Concurrent design of hierarchical materials and structures , 2008 .

[20]  John Hinde,et al.  Statistical Modelling in R , 2009 .

[21]  Surya R. Kalidindi,et al.  Multi-scale modeling of elastic response of three-dimensional voxel-based microstructure datasets using novel DFT-based knowledge systems , 2010 .

[22]  Surya R. Kalidindi,et al.  A new framework for computationally efficient structure–structure evolution linkages to facilitate high-fidelity scale bridging in multi-scale materials models , 2011 .

[23]  Yuksel C. Yabansu,et al.  Understanding and visualizing microstructure and microstructure variance as a stochastic process , 2011 .

[24]  Clément Farabet,et al.  Torch7: A Matlab-like Environment for Machine Learning , 2011, NIPS 2011.

[25]  S. Ahzi,et al.  Using SAXS approach to estimate thermal conductivity of polystyrene/zirconia nanocomposite by exploiting strong contrast technique , 2011 .

[26]  Gang Wang,et al.  Effective elastic properties of nanoporous materials with hierarchical structure , 2011 .

[27]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[28]  David L. McDowell,et al.  Linking phase field and finite element modeling for process-structure-property relations of a Ni-base superalloy , 2012 .

[29]  David T. Fullwood,et al.  Microstructure Sensitive Design for Performance Optimization , 2012 .

[30]  S. Kalidindi,et al.  Novel microstructure quantification framework for databasing, visualization, and analysis of microstructure data , 2013, Integrating Materials and Manufacturing Innovation.

[31]  Jitesh H. Panchal,et al.  Key computational modeling issues in Integrated Computational Materials Engineering , 2013, Comput. Aided Des..

[32]  Krishna Rajan,et al.  Informatics for Materials Science and Engineering: Data-Driven Discovery for Accelerated Experimentation and Application , 2013 .

[33]  Gerald Penn,et al.  Convolutional Neural Networks for Speech Recognition , 2014, IEEE/ACM Transactions on Audio, Speech, and Language Processing.

[34]  Surya R. Kalidindi,et al.  A data-driven approach to establishing microstructure–property relationships in porous transport layers of polymer electrolyte fuel cells , 2014 .

[35]  Surya R. Kalidindi,et al.  Materials Data Science: Current Status and Future Outlook , 2015 .

[36]  Xiang Zhang,et al.  Character-level Convolutional Networks for Text Classification , 2015, NIPS.

[37]  Olga Wodo,et al.  Automated, high throughput exploration of process–structure–property relationships using the MapReduce paradigm , 2015 .

[38]  Surya R. Kalidindi,et al.  Hierarchical Materials Informatics: Novel Analytics for Materials Data , 2015 .

[39]  Surya R. Kalidindi,et al.  Structure–property linkages using a data science approach: Application to a non-metallic inclusion/steel composite system , 2015 .

[40]  Surya R. Kalidindi,et al.  Representation and calibration of elastic localization kernels for a broad class of cubic polycrystals , 2015 .

[41]  Surya R. Kalidindi,et al.  Efficient computation of the angularly resolved chord length distributions and lineal path functions in large microstructure datasets , 2016 .

[42]  J. Eckert,et al.  Structure-property relationships in nanoporous metallic glasses , 2016 .

[43]  Surya R. Kalidindi,et al.  Versatile algorithms for the computation of 2-point spatial correlations in quantifying material structure , 2016, Integrating Materials and Manufacturing Innovation.

[44]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[45]  Paul Raccuglia,et al.  Machine-learning-assisted materials discovery using failed experiments , 2016, Nature.

[46]  Surya R. Kalidindi,et al.  Quantification and classification of microstructures in ternary eutectic alloys using 2-point spatial correlations and principal component analyses , 2016 .

[47]  Ankit Agrawal,et al.  Context Aware Machine Learning Approaches for Modeling Elastic Localization in Three-Dimensional Composite Microstructures , 2017, Integrating Materials and Manufacturing Innovation.

[48]  Yuksel C. Yabansu,et al.  Extraction of reduced-order process-structure linkages from phase-field simulations , 2017 .

[49]  David B. Brough,et al.  Extraction of Process-Structure Evolution Linkages from X-ray Scattering Measurements Using Dimensionality Reduction and Time Series Analysis , 2017, Integrating Materials and Manufacturing Innovation.

[50]  Surya R. Kalidindi,et al.  Extracting knowledge from molecular mechanics simulations of grain boundaries using machine learning , 2017 .

[51]  A. Çeçen,et al.  Development of high throughput assays for establishing process-structure-property linkages in multiphase polycrystalline metals: Application to dual-phase steels , 2017 .

[52]  Michael Selzer,et al.  Data science approaches for microstructure quantification and feature identification in porous membranes , 2017 .