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RESEARCH PAPER · Acta Materialia · 2017

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

Ahmet Cecen, Hanjun Dai, Yuksel C. Yabansu, Surya R. Kalidindi, Le Song

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Material structure-property linkages using three-dimensional convolutional neural networksOriginalDeep learning approaches for mining structure-property linkages in high contrast composites from simulation datasets365A Spectral Density Function Approach for Active Layer Design of Organic Photovoltaic Cells26A new framework for rotationally invariant two-point spatial correlations in microstructure datasets43Interdisciplinary Research on Designing Engineering Material Systems: Results From a National Science Foundation Workshop13Seeing permeability from images: fast prediction with convolutional neural networks212Extracting Grain Orientations from EBSD Patterns of Polycrystalline Materials Using Convolutional Neural Networks66Modelling feasibility constraints for materials design: Application to inverse crystallographic texture problem10ElemNet: Deep Learning the Chemistry of Materials From Only Elemental Composition510Using deep neural network with small dataset to predict material defects512Analysis the Need of Machine Learning in Predicting the Mechanical Properties of Nanomaterials Model1Establishing structure-property localization linkages for elastic deformation of three-dimensional high contrast composites using deep learning approaches192Deep Learning: New Engine for the Study of Material Microstructures and Physical Properties3Materials informatics: From the atomic-level to the continuum191Heterogeneous materials design in additive manufacturing: Model calibration and uncertainty-guided model selection22Microstructural damage sensitivity prediction using spatial statistics12Progress Report on Phase Separation in Polymer Solutions190A Comparative Study of the Efficacy of Local/Global and Parametric/Nonparametric Machine Learning Methods for Establishing Structure–Property Linkages in High-Contrast 3D Elastic Composites25Convolutional Neural Networks for Crystal Material Property Prediction Using Hybrid Orbital-Field Matrix and Magpie Descriptors52Deep learning methods based on cross-section images for predicting effective thermal conductivity of composites3On-the-Fly Adaptivity for Nonlinear Twoscale Simulations Using Artificial Neural Networks and Reduced Order Modeling62A Bayesian framework for materials knowledge systems29A Review of the Application of Machine Learning and Data Mining Approaches in Continuum Materials Mechanics403Clustering discretization methods for generation of material performance databases in machine learning and design optimization112Bayesian Framework for Inverse Inference in Manufacturing Process Chains4Deep materials informatics: Applications of deep learning in materials science294Machine Learning-Based Classification of Dislocation Microstructures42Predicting Microstructure-Sensitive Fatigue-Crack Path in 3D Using a Machine Learning Framework61Data-driven crack assessment based on surface measurements4Deep learning based predictive modeling for structure-property linkages34Machine learning in materials 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