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RESEARCH PAPER · Frontiers in Materials · 2022

Digital Twins for Materials

Surya R. Kalidindi, Michael O. Buzzy, Brad L. Boyce, Rémi Dingreville

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Digital Twins for MaterialsOriginalComposition and Characteristics in Hydrocarbons, Oil and Gas Fields3Current role of the digital transformation for improved materials and material utilization0Untitled work0West Siberian Oil and Gas Field – Metal Content of Oil and Processed Products0The road to 3-dim nanomagnetism: Steep curves and architectured crosswalks23Non-Destructive Health Monitoring of Structural Polymer Composites: Trends and Perspectives in the Digital Era30The adsorption modeling of bisphenol A derivatives on the surface of carbon materials3Void nucleation during ductile rupture of metals: A review183Electronic Passport as the Basis of the Digital Twin7Molecular Dynamics Study of Cured ED-20 Epoxy Resin for Predicting the Glass Transition Temperature and Relationship with Structure Features13Establishing structure–property linkages for wicking time predictions in porous polymeric membranes using a data-driven approach2AFM SMILER: A Scale Model Interactive Learning Extended Reality Toolkit for Atomic Force Microscopy Created With Digital Twin Technology8A data-driven modeling approach to quantify morphology effects on transport properties in nanostructured NMC particles15Insights into Plastic Localization by Crystallographic Slip from Emerging Experimental and Numerical Approaches51Data‐driven and artificial intelligence accelerated steel material research and intelligent manufacturing technology78Communicating Supraparticles to Enable Perceptual, Information‐Providing Matter11Ontologies in digital twins: A systematic literature review119Harnessing the Power of Digital Twins for Enhanced Material Behavior Prediction and Manufacturing Process Optimization in Materials Engineering1Digital Twins: Revolutionizing Automotive Supply Chains1MULTISCALE MODELING AND COMPUTER-AIDED DESIGN OF ADVANCED MATERIALS WITH HIERARCHICAL STRUCTURE4Towards Zero-Defect and Zero-Waste Manufacturing by Implementing Non-Destructive Inspection Technologies1AI methods in materials design, discovery and manufacturing: A review142Driving next generation manufacturing through advanced metals characterisation capability7Unlocking the Hidden Potential of the Data-Driven Nanocarbon Genome: Unleashing Novel Neural Pathways, Elucidating Growth-Property Relationships, and Enabling Inverse Design0Fast prototype and rapid construction of three-dimensional and multi-scaled pitcher for controlled drainage by systematic biomimicry5Semantics-Enabled Data Federation: Bringing Materials Scientists Closer to FAIR Data10Development of a digital material shadow for the press hardening route of medium manganese steel2Application of digital twins for simulation based tailoring of laser induced graphene12AnisoGNN: Graph neural networks generalizing to anisotropic properties of polycrystals7Anomaly Detection in Materials Digital Twins with Multiscale ICME for Additive Manufacturing1Synergizing Theoretical Model Development and Experimentation through the Bayesian Co-navigation Workflow0High-fidelity phase-field simulation of solid-state sintering enabled by Bayesian data assimilation using in situ electron tomography data23Human-Guided Metaverse Synthesis for Quantum Dots: Advancing Nanomaterial Research through Augmented Artificial Intelligence2Bayesian Conavigation: Dynamic Designing of the Material Digital Twins via Active Learning17Enabling the vision of a perpetual innovative product – predicting function fulfillment of new product generations in a circular factory9Unsupervised physics-informed disentanglement of multimodal materials data10Multiscale Modeling and Computer-Aided Design of Advanced Materials with Hierarchical Structure11Reproducing hydrodynamics and elastic objects: A hybrid mesh-free model framework for dynamic multi-phase flows2Active learning for the design of polycrystalline textures using conditional normalizing flows6Semantic Orchestration and Exploitation of Material Data: A Dataspace Solution Demonstrated on Steel and Copper Applications13Applying Digital Twin Methods for Process-Structure-Property Correlation Assessment in Metal Additive Manufacturing with Limited Experimental Data2AI-Driven Autonomous Platforms for Nanomaterial Discovery and Optimization: Bridging Simulation, Synthesis, and In Situ Characterization2Development of a soft sensor for determining the cooling rate for press hardening of medium manganese steel0Digital Twins and Neural Networks: A Powerful Toolkit for Developing Fretting Fatigue‐Resistant Materials4The Future of Materials Science with AI: Self-Driving Laboratories and Closed-Loop Discovery0Geometrical shape learning as basis for compact microstructure representations and microstructure-properties linkages0Towards Zero Defect and Zero Waste Manufacturing by Implementing Non-Destructive Inspection Technologies19Thermodynamically-Informed Iterative Neural Operators for heterogeneous elastic localization5A digital twin framework for turbine blade crack propagation prediction17Data-driven stochastic 3D modeling of the nanoporous binder-conductive additive phase in battery cathodes2Generative AI at the Crossroads: Light Bulb, Dynamo, or Microscope?0Artificial intelligence-assisted next-generation biomaterials: From design and preparation to medical applications29Analysis of damage mechanisms in an AlSi10Fe0.7 model casting alloy based on a “digital twin”1Materials discovery in combinatorial and high-throughput synthesis and processing: A new Frontier for SPM9Advances in intelligent epitaxy of semiconductor materials1Void nucleation in heterogeneous materials induced by particle-matrix debonding in polycrystalline matrix0Multiscale computational modeling of mechanical properties of polymer composite materials0A modeling method for terahertz scattering on rough dielectric surfaces based on deep learning and physical optics approximation0Nanomaterials Discovery Using AI-Based Technology Accelerators2Tecnología de materiales0Advanced Characterization of Sustainable HEAs0Digital twin implementation and case studies0Emerging Challenges and Future Directions in Multiscale Modelling for Integration of Biology and Materials Design1Industry 4.0 and Eco-materials: Opportunities and Challenges0Introduction0Probabilistic Materials Informatics0The statistical finite element method: A theoretical foundation for digital twins1Materials and System Design for Self‐Decision Bioelectronic Systems15Review on Predictive Models and Integration Strategies for Holistic Impact Assessment of Chemicals and Materials11AI-powered non-destructive testing for smart manufacturing of carbon-negative biopolymer-bound soil composite2Measuring complexity in pre-nucleation metal fatigue via finite element simulations of microplasticity, surrogate modeling, and residual analysis0Digital twin for in – space manufacturing: a comprehensive review from framework to future5Multi-modal predictions of material degradation: A case study on friction evolution4Virtual interactive simulation of flexible tubing systems for robotic arms0Materials Engineering of Elastomer–Metal Interfaces Under Corrosion, Wear, and Adhesion Stressors0Towards the Development of Multiscale Digital Twins for Fiber-Reinforced Composite Materials Using Machine Learning0An AI-Based Spoken English Evaluation System Using Speech Recognition Technology: A Reproducible Pipeline and Evidence from ArL2Eng0Digital twin construction for biomimetic materials2Roadmap on novel computational approaches for bridging length and time scales: addressing challenges in modeling processes, characterization, and performance of metals and alloys1Data-Driven Engineering of Antimicrobial Nanomaterials for Food Safety and Biomedical Systems8Simulation-informed deep learning for estimating material parameters from microstructural morphology0Digital Metallurgical Engineering: A Scientometric Review of Progress, Applications, and Prospects0Inverse design of 2.5D interlock composite architectures using a constrained stochastic framework0Machine Learning for Structural Steels: Materials Design, Property Prediction, Durability, and Future Directions0A perspective on component-level digital twins for surface marine propulsion system health monitoring: need and feasibility0Generative artificial intelligence for nanomaterial discovery and nanoengineering: From data-driven design to autonomous laboratories0AI-driven property prediction and design optimisation of composite materials: A review0Low-Density Materials for Particle Accelerator Applications: Challenges, Capabilities, and Future Directions0A physics-chemistry-materials-informed neural digital twin for autonomous design, characterization, and optimization of green-synthesized antibacterial silver nanoparticles0Surface Engineering Applications Using Nanocomposite Coatings for Industrial Manufacturing Components0

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  2. Current role of the digital transformation for improved materials and material utilization

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  3. Untitled work

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  4. West Siberian Oil and Gas Field – Metal Content of Oil and Processed Products

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  5. The road to 3-dim nanomagnetism: Steep curves and architectured crosswalks

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  7. The adsorption modeling of bisphenol A derivatives on the surface of carbon materials

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  8. Void nucleation during ductile rupture of metals: A review

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  10. Molecular Dynamics Study of Cured ED-20 Epoxy Resin for Predicting the Glass Transition Temperature and Relationship with Structure Features

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  11. Establishing structure–property linkages for wicking time predictions in porous polymeric membranes using a data-driven approach

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  12. AFM SMILER: A Scale Model Interactive Learning Extended Reality Toolkit for Atomic Force Microscopy Created With Digital Twin Technology

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  13. A data-driven modeling approach to quantify morphology effects on transport properties in nanostructured NMC particles

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  14. Insights into Plastic Localization by Crystallographic Slip from Emerging Experimental and Numerical Approaches

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  15. Data‐driven and artificial intelligence accelerated steel material research and intelligent manufacturing technology

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  16. Communicating Supraparticles to Enable Perceptual, Information‐Providing Matter

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  17. Ontologies in digital twins: A systematic literature review

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  18. Harnessing the Power of Digital Twins for Enhanced Material Behavior Prediction and Manufacturing Process Optimization in Materials Engineering

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  19. Digital Twins: Revolutionizing Automotive Supply Chains

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