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RESEARCH PAPER · MRS Communications · 2019

Deep materials informatics: Applications of deep learning in materials science

Ankit Agrawal, Alok Choudhary

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Deep materials informatics: Applications of deep learning in materials scienceOriginalProperty Prediction of Organic Donor Molecules for Photovoltaic Applications Using Extremely Randomized Trees37A Real-Time Iterative Machine Learning Approach for Temperature Profile Prediction in Additive Manufacturing Processes65Martensite Start Temperature Predictor for Steels Using Ensemble Data Mining9Enhancing materials property prediction by leveraging computational and experimental data using deep transfer learning339Automated Classification of Helium Ingress in Irradiated X-7504Graph convolutional neural networks with global attention for improved materials property prediction257Integrating Deep Learning in Domain Sciences at Exascale6Design space visualization for guiding investments in biodegradable and sustainably sourced materials7A handle on the scandal: Data driven approaches to structure prediction4Evolving the Materials Genome: How Machine Learning Is Fueling the Next Generation of Materials Discovery99Theory-training deep neural networks for an alloy solidification benchmark problem39Deep learning analysis on microscopic imaging in materials science195Automated Detection of Helium Bubbles in Irradiated X-75048Crystal symmetry classification from powder X-ray diffraction patterns using a convolutional neural network33Advances in neural networks and potential for their application to steel metallurgy32Deep Learning-based Phase Prediction of High-Entropy Alloys10Statistical characterization and reconstruction of heterogeneous microstructures using deep neural network48Emerging materials intelligence ecosystems propelled by machine learning440Polymer informatics: Current status and critical next steps272Active learning and element-embedding approach in neural networks for infinite-layer versus perovskite oxides13Deep Learning Approach for Chemistry and Processing History Prediction From Materials Microstructure: Application to Spinodal Decomposition1Heterogeneous Feature Fusion Based Machine Learning on Shallow-Wide and Heterogeneous-Sparse Industrial Datasets1Machine Learning-based Microstructure Prediction During Laser Sintering of Alumina: A Conditional Generative Approach2Machine learning for materials design and discovery128Surface science of cosmetic substrates, cleansing actives and formulations68Enabling deeper learning on big data for materials informatics applications67A micromechanics-based artificial neural networks model for elastic properties of short fiber composites82Machine Learning for Transition-Metal-Based Hydrogen Generation Electrocatalysts122Deep Learning-based Face Mask Detection Using Automated GUI for COVID-194Casting Microstructure Inspection Using Computer Vision: Dendrite Spacing in Aluminum Alloys22Machine learning-based microstructure prediction during laser sintering of alumina41Bandgap Engineering in the Configurational Space of Solid Solutions via Machine Learning: (Mg,Zn)O Case Study13From microstructural images to properties - an interpretable deep learning approach to predict elastic-plastic properties of fiber composites4Deep learning-based estimation of Flory–Huggins parameter of A–B block copolymers from cross-sectional images of phase-separated structures17Convolutional neural network model for synchrotron radiation imaging datasets to automatically detect interfacial microstructure: An in situ process monitoring tool during solar PV ribbon fabrication12Computational discovery of energy materials in the era of big data and machine learning: A critical review60Catalyze Materials Science with Machine Learning66Analysis on Microstructure–Property Linkages of Filled Rubber Using Machine Learning and Molecular Dynamics Simulations6Automated stopping criterion for spectral measurements with active learning28Exploration of the Underlying Space in Microscopic Images via Deep Learning for Additively Manufactured Piezoceramics22ChemDataExtractor 2.0: Autopopulated Ontologies for Materials Science152Machine learning and materials informatics approaches in the analysis of physical properties of carbon nanotubes: A review113Performance, Successes and Limitations of Deep Learning Semantic Segmentation of Multiple Defects in Transmission Electron Micrographs2An Incremental Learning for Plant Disease classification3Review of machine learning‐driven design of polymer‐based dielectrics50Featurization Strategies for Polymer Sequence or Composition Design by Machine Learning2Cross-property deep transfer learning framework for enhanced predictive analytics on small materials data197Data-driven thermoelectric modeling: Current challenges and prospects25Practical Aspects of the Design and Use of the Artificial Neural Networks in Materials Engineering24Estimating the performance of a material in its service space via Bayesian active learning: a case study of the damping capacity of Mg alloys14Evaluation of Performance of Chlorinated Polyethylene Using Wireless Network and Artificial Intelligence Technology5Featurization strategies for polymer sequence or composition design by machine learning96Materials property prediction using feature selection based machine learning technique11Prediction of material property using optimized augmented graph-attention layer in GNN17Scientific Validation in Materials AI—A Critical Survey of Conceptual Approaches: A Review Study0Fatigue life prediction of aluminum alloy via knowledge-based machine learning149Attribution-Driven Explanation of the Deep Neural Network Model via Conditional Microstructure Image Synthesis9Machine learning in nuclear materials research125Chemical Composition Data‐Driven Machine‐Learning Prediction for Phase Stability and Materials Properties of Inorganic Crystalline Solids9Design of Phononic Bandgap Metamaterials Based on Gaussian Mixture Beta Variational Autoencoder and Iterative Model Updating43On the application of physics informed neural networks (PINN) to solve boundary layer thermal-fluid problems140Large scale dataset of real space electronic charge density of cubic inorganic materials from density functional theory (DFT) calculations14A deep learning-aided prediction approach for creep rupture time of Fe–Cr–Ni heat-resistant alloys by integrating textual and visual features34Quantifying transfer learning synergies in infinite-layer and perovskite nitrides, oxides, and fluorides6Convolutional neural networks for expediting the determination of minimum volume requirements for studies of microstructurally small cracks, Part I: Model implementation and predictions14Casting Defects Detection in Aluminum Alloys Using Deep Learning: a Classification Approach51Deep learning approach for chemistry and processing history prediction from materials microstructure22Materials structure–property factorization for identification of synergistic phase interactions in complex solar fuels photoanodes7Recent advances and applications of deep learning methods in materials science1161Data-Driven Multi-Scale Modeling and Optimization for Elastic Properties of Cubic Microstructures11Deep Learning CNN for the Prediction of Grain Orientations on EBSD Patterns of AA5083 Alloy5Performance and limitations of deep learning semantic segmentation of multiple defects in transmission electron micrographs42Stimulus: Accelerate Data Management for Scientific AI applications in HPC6Using Multi-Resolution Data to Accelerate Neural Network Training in Scientific Applications4A virtual sample generation algorithm supporting machine learning with a small-sample dataset: A case study for rubber materials79Deep learning object detection in materials science: Current state and future directions82Processing time, temperature, and initial chemical composition prediction from materials microstructure by deep network for multiple inputs and fused data17Materials informatics for dielectric loss tangent in the millimeter wave region5Surrogate- and invariance-boosted contrastive learning for data-scarce applications in science20Artificial Intelligence‐Based Material Discovery for Clean Energy Future46End-to-end ancient ceramic classification toolkit based on deep learning: A case study of black glazed wares of Jian kilns (Song Dynasty, Fujian province)15Correlated RNN Framework to Quickly Generate Molecules with Desired Properties for Energetic Materials in the Low Data Regime40Deep learning framework for uncovering compositional and environmental contributions to pitting resistance in passivating alloys38A machine learning approach to predict the structural and magnetic properties of Heusler alloy families24A deep learning framework for layer-wise porosity prediction in metal powder bed fusion using thermal signatures43Electrochemoinformatics as an Emerging Scientific Field for Designing Materials and Electrochemical Energy Storage and Conversion Devices—An Application in Battery Science and Technology24Estimation of surface roughness in selective laser sintering using computational models11Generative Adversarial Networks and Mixture Density Networks-Based Inverse Modeling for Microstructural Materials Design22Deep Learning of Microstructures0To Shuffle or Not To Shuffle: Mini-Batch Shuffling Strategies for Multi-class Imbalanced Classification3Which Deep Learning Framework Should I Use: A Comparative Study For Deep Regression Modeling3A surrogate model for the prediction of permeabilities and flow through porous media: a machine learning approach based on stochastic Brownian motion14Capacity-prediction models for organic anode-active materials of lithium-ion batteries: advances in predictors using small data10Interpretable models for extrapolation in scientific machine learning97Supervised machine learning techniques to protect IoT healthcare environment against cyberattacks9Bibliometric Analysis of Fourth Industrial Revolution Applied to Material Sciences Based on Web of Science and Scopus Databases from 2017 to 202123Data-Driven Design of Polymer-Based Biomaterials: High-throughput Simulation, Experimentation, and Machine Learning109Discrete learning-based intelligent methodology for heart disease diagnosis6A surrogate model for the prediction of permeabilities and flow through porous media3AtomVision: A Machine Vision Library for Atomistic Images14Exploring supervised machine learning for multi-phase identification and quantification from powder X-ray diffraction spectra36Predicting thermoelectric transport properties from composition with attention-based deep learning25An improved Artificial Neural Network for the direct prediction of fretting fatigue crack initiation lifetime38Evaluating the transferability of machine-learned force fields for material property modeling5MPpredictor: An Artificial Intelligence-Driven Web Tool for Composition-Based Material Property Prediction36Predicting the Melting Point of Energetic Molecules Using a Learnable Graph Neural Fingerprint Model19Prediction of compressive mechanical properties of three-dimensional mesoscopic aluminium foam based on deep learning method45A data-driven framework for permeability prediction of natural porous rocks via microstructural characterization and pore-scale simulation37Deep learning method of stochastic reconstruction of three-dimensional digital cores from a two-dimensional image15Improving deep learning model performance under parametric constraints for materials informatics applications12An optimization-based supervised learning algorithm for PXRD phase fraction estimation2A unified microstructure segmentation approach via human-in-the-loop machine learning39AI for Learning Deformation Behavior of a Material: Predicting Stress-Strain Curves 4000x Faster Than Simulations5Pre-Activation based Representation Learning to Enhance Predictive Analytics on Small Materials Data7An AI-driven microstructure optimization framework for elastic properties of titanium beyond cubic crystal systems18Investigating the impact of data scaling on the k-nearest neighbor algorithm24Screening outstanding mechanical properties and low lattice thermal conductivity using global attention graph neural network24Needs, Trends, and Advances in Scintillators for Radiographic Imaging and Tomography98A Novel Discrete Deep Learning–Based Cancer Classification Methodology5Deep learning modeling in microscopy imaging: A review of materials science applications62Classification and Detection of Rice Diseases Using a 3-Stage CNN Architecture with Transfer Learning Approach49Machine learning for predicting ZT values of high-performance thermoelectric materials in mid-temperature range17Physics-based Data-Augmented Deep Learning for Enhanced Autogenous Shrinkage Prediction on Experimental Dataset5Enhancing corrosion-resistant alloy design through natural language processing and deep learning77Evolution of artificial intelligence for application in contemporary materials science27Predicting fatigue crack growth metrics from fractographs: Towards fractography by computer vision22Application of Machine Learning in Material Synthesis and Property Prediction165Artificial Intelligence in Predicting Mechanical Properties of Composite Materials249Data science and material informatics in physical metallurgy and material science: An overview of milestones and limitations32Data-driven investigation to model the corrosion inhibition efficiency of Pyrimidine-Pyrazole hybrid corrosion inhibitors52Nonlinear probabilistic virtual sample generation using Gaussian process latent variable model and fitting for rubber material6A data generation approach for surrogate models for magneto‐static simulations0Machine learning investigation to predict corrosion inhibition capacity of new amino acid compounds as corrosion inhibitors59Thermal diffusion in discontinuous media: A hybrid peridynamics-based machine learning model8Construction of automated optimal control systems with elements of artificial intelligence1Superior Prediction of Graphene Nanoflake Properties withUnbiased Graph Embedding2Computational materials discovery and development for Li and non-Li advanced battery chemistries8Analysis of the Possibility of Using Selected Tools and Algorithms in the Classification and Recognition of Type of Microstructure3Examining the microstructure, morphological features, and wetting characteristics of Ti/TiN/TiAlN thin films produced through RF/DC magnetron co-sputtering18Advances in materials informatics for tailoring thermal radiation: A perspective review2Aprendizaje automático aplicado a la caracterización de nanoestructuras base carbono0Size-Dependent Elastic Properties of Nanofilms: Exponentially Decreased Surface Elasticity Model1A Deep Learning Framework for Time-Series Processing-Microstructure-Property Prediction3Models Supporting Cast Iron Production Based on Artificial Intelligence and Prediction Methods0Predicting Grain Orientations of 316 Stainless Steel Using Convolutional Neural Networks0Structure-aware graph neural network based deep transfer learning framework for enhanced predictive analytics on diverse materials datasets90Advances in materials informatics: a review42Accelerating materials discovery: combinatorial synthesis, high-throughput characterization, and computational advances77Machine Learning Design of Perovskite Catalytic Properties83Augmentation of scarce data—A new approach for deep-learning modeling of composites31

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  1. Property Prediction of Organic Donor Molecules for Photovoltaic Applications Using Extremely Randomized Trees

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  2. A Real-Time Iterative Machine Learning Approach for Temperature Profile Prediction in Additive Manufacturing Processes

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  3. Martensite Start Temperature Predictor for Steels Using Ensemble Data Mining

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  4. Enhancing materials property prediction by leveraging computational and experimental data using deep transfer learning

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  5. Automated Classification of Helium Ingress in Irradiated X-750

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  43. Performance, Successes and Limitations of Deep Learning Semantic Segmentation of Multiple Defects in Transmission Electron Micrographs

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  44. An Incremental Learning for Plant Disease classification

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  47. Cross-property deep transfer learning framework for enhanced predictive analytics on small materials data

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  49. Practical Aspects of the Design and Use of the Artificial Neural Networks in Materials Engineering

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  50. Estimating the performance of a material in its service space via Bayesian active learning: a case study of the damping capacity of Mg alloys

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