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RESEARCH PAPER · Integrating materials and manufacturing innovation · 2018

Perspectives on the Impact of Machine Learning, Deep Learning, and Artificial Intelligence on Materials, Processes, and Structures Engineering

Dennis M. Dimiduk, Elizabeth A. Holm, Stephen R. Niezgoda

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Perspectives on the Impact of Machine Learning, Deep Learning, and Artificial Intelligence on Materials, Processes, and Structures EngineeringOriginalHigh-resolution, yet statistically relevant, analysis of damage in DP steel using artificial intelligence2A review of deep learning in the study of materials degradation182Comparative analysis of artificial intelligence techniques for material selection applied to manufacturing in Industry 4.038Nanoinformatics, and the big challenges for the science of small things92Prediction of Electroencephalogram Time Series With Electro-Search Optimization Algorithm Trained Adaptive Neuro-Fuzzy Inference System18Detecting void-induced scatter of fatigue life of selective laser melting-fabricated inconel 718 using miniature specimens18Understanding grain boundaries – The role of crystallography, structural descriptors and machine learning38The evolving landscape for alloy design29The Impact of Artificial Intelligence on the Accounting Industry39Large-area, high-resolution characterisation and classification of damage mechanisms in dual-phase steel using deep learning70Exploring effective charge in electromigration using machine learning27Bayesian Framework for Inverse Inference in Manufacturing Process Chains4Exploring new links between crystal plasticity models and high-energy X-ray diffraction microscopy46Stakeholder related fields of action for process optimization of nearly zero energy and plus energy buildings2Recent advances and applications of machine learning in solid-state materials science2607Modeling and optimization of the crushing behavior and energy absorption of plain weave composite hexagonal quadruple ring systems using artificial neural network43Associating local microstructure with predicted thermally-induced stress hotspots using convolutional neural networks19Identification of advanced spin-driven thermoelectric materials via interpretable machine learning81Review: Materials Ecosystem for Additive Manufacturing Powder Bed Fusion Processes32Multi-channel convolutional neural networks for materials properties prediction31Detecting Defects in Materials Using Deep Convolutional Neural Networks2Machine learning reveals multiple classes of diamond nanoparticles25Prediction of Physical and Mechanical Properties for Metallic Materials Selection Using Big Data and Artificial Neural Networks41The Materials Simulation Toolkit for Machine learning (MAST-ML): An automated open source toolkit to accelerate data-driven materials research78Harnessing Legacy Data to Educate Data-Enabled Structural Materials Engineers4Critical Temperature Prediction for a Superconductor: A Variational Bayesian Neural Network Approach41Trends in 3D Printing Processes for Biomedical Field: Opportunities and Challenges210Development of machine learning implementation in engineering education: A literature review2Unraveling pore evolution in post-processing of binder jetting materials: X-ray computed tomography, computer vision, and machine learning87Opportunities and Challenges for Machine Learning in Materials Science523Prediction of the evolution of the stress field of polycrystals undergoing elastic-plastic deformation with a hybrid neural network model62Hard X-ray phase-contrast-enhanced micro-CT for quantifying interfaces within brittle dense root-filling-restored human teeth7Recent progress on discovery and properties prediction of energy materials: Simple machine learning meets complex quantum chemistry62Artificial Intelligence (AI), Disability, and Telemedicine/Telehealth1Deep learning analysis on microscopic imaging in materials science195Scientific AI in materials science: a path to a sustainable and scalable paradigm120Selecting machine learning models for metallic nanoparticles35Featureless approach for predicting Critical Temperature of Superconductors5Prediction of the Bilinear Stress-Strain Curve of Aluminum Alloys Using Artificial Intelligence and Big Data40Extracting local nucleation fields in permanent magnets using machine learning36Interpretability of Deep Learning Classification for Low-Carbon Steel Microstructures23Cheminformatics for accelerated design of chemical admixtures13Machine learning materials physics: Multi-resolution neural networks learn the free energy and nonlinear elastic response of evolving microstructures74Microstructure representation learning using Siamese networks3Visualizing Artificial Intelligence Used in Education Over Two Decades45Prediction of Fatigue Crack Growth Behaviour in Ultrafine Grained Al 2014 Alloy Using Machine Learning52Artificial Intelligence in Materials Modeling and Design98How machine learning can help the design and analysis of composite materials and structures?6Machine Learning Tools to Predict Hot Injection Syntheses Outcomes for II–VI and IV–VI Quantum Dots37Advances in neural networks and potential for their application to steel metallurgy32Prediction of Mechanical Properties by Artificial Neural Networks to Characterize the Plastic Behavior of Aluminum Alloys70The pure and representative types of disordered platinum nanoparticles from machine learning12Multiscale computation on feedforward neural network and recurrent neural network53Pseudo stereo output synthesis of wind instruments based on computer multimedia technology0WITHDRAWN: A cavernous analytics using advanced machine learning for real world datasets in research implementations4Microstructure Generation via Generative Adversarial Network for Heterogeneous, Topologically Complex 3D Materials121Design deep neural network architecture using a genetic algorithm for estimation of pile bearing capacity84An Insight into Tool and Software Used in AI, Machine Learning and Data Analytics3Automated Process Synthesis Using Reinforcement Learning2Deep Belief Network‐Based Multifeature Fusion Music Classification Algorithm and Simulation14Deep learning for material synthesis and manufacturing systems: A 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solver for heterogeneous solids1A contemporary approach to the MSE paradigm powered by Artificial Intelligence from a review focused on Polymer Matrix Composites28Skeletal-based microstructure representation and convolution reconstruction3Machine Learning in Cyber-Physical Systems and Manufacturing Singularity – It Does Not Mean Total Automation, Human is Still in the Centre: Part II – In-CPS and a View from Community on Industry 4.0 Impact on Society11Unsupervised Machine Learning Via Transfer Learning and k-Means Clustering to Classify Materials Image Data101Advances in the Development of Sol-Gel Materials Combining Small-Angle X-ray Scattering (SAXS) and Machine Learning (ML)12Covid-19 Pandemic and Investigation of Artificial Intelligence Applications Used in the Pandemic1Green Algorithms: Quantifying the Carbon Footprint of Computation543An integrated texture analysis and machine learning approach for durability assessment of lightweight cement composites with hydrophobic coatings 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Performance, and Future Trends18Artificial intelligence in single screw polymer extrusion: Learning from computational data17Improving Industrial Robot Positioning Accuracy to the Microscale Using Machine Learning Method33Shrink–swell index prediction through deep learning7Empowering engineering with data, machine learning and artificial intelligence: a short introductive review33Prediction of Mechanical Properties of Sensitized Stainless Steel by Neural Network Modeling and Validation Using Ball Indentation Test4Machine Learning for Software Developers: An Introductory Guide0

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  1. High-resolution, yet statistically relevant, analysis of damage in DP steel using artificial intelligence

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  2. A review of deep learning in the study of materials degradation

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  6. Detecting void-induced scatter of fatigue life of selective laser melting-fabricated inconel 718 using miniature specimens

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  16. Modeling and optimization of the crushing behavior and energy absorption of plain weave composite hexagonal quadruple ring systems using artificial neural network

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  17. Associating local microstructure with predicted thermally-induced stress hotspots using convolutional neural networks

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  18. Identification of advanced spin-driven thermoelectric materials via interpretable machine learning

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  19. Review: Materials Ecosystem for Additive Manufacturing Powder Bed Fusion Processes

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  43. Machine learning materials physics: Multi-resolution neural networks learn the free energy and nonlinear elastic response of evolving microstructures

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  44. Microstructure representation learning using Siamese networks

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  45. Visualizing Artificial Intelligence Used in Education Over Two Decades

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  46. Prediction of Fatigue Crack Growth Behaviour in Ultrafine Grained Al 2014 Alloy Using Machine Learning

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  47. Artificial Intelligence in Materials Modeling and Design

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  48. How machine learning can help the design and analysis of composite materials and structures?

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  49. Machine Learning Tools to Predict Hot Injection Syntheses Outcomes for II–VI and IV–VI Quantum Dots

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