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RESEARCH PAPER · Nano Futures · 2019

Artificial intelligence design algorithm for nanocomposites optimized for shear crack resistance

Chi-Hua Yu, Zhao Qin, Markus J. Buehler

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Artificial intelligence design algorithm for nanocomposites optimized for shear crack resistanceOriginalBiological Material Interfaces as Inspiration for Mechanical and Optical Material Designs182Nanoengineering in biomedicine: Current development and future perspectives64Sonification based de novo protein design using artificial intelligence, structure prediction, and analysis using molecular modeling50Designing an Adhesive Pillar Shape with Deep Learning-Based Optimization68Using Deep Learning to Predict Fracture Patterns in Crystalline Solids178Digital and experimental synergies to design high-heeled shoes11Polymer nanodielectrics—Short history and future perspective30A semi-supervised approach to architected materials design using graph neural networks73WebNet: A biomateriomic three-dimensional spider web neural net18Hydration-induced reversible deformation of biological materials129Surrogate Model via Artificial Intelligence Method for Accelerating Screening Materials and Performance Prediction39Artificial intelligence and machine learning in design of mechanical materials690Exploring the potential of transfer learning for metamodels of heterogeneous material deformation21Characterising soft matter using machine learning52Pragmatic generative optimization of novel structural lattice metamaterials with machine learning121Taking the leap between analytical chemistry and artificial intelligence: A tutorial review175Deep learning model to predict complex stress and strain fields in hierarchical composites389End-to-end deep learning method to predict complete strain and stress tensors for complex hierarchical composite microstructures147A stochastic multiscale method for the prediction of the thermal conductivity of Polymer nanocomposites through hybrid machine learning algorithms191Autonomous reinforcement learning agent for stretchable kirigami design of 2D materials37Deep learning approach to assess damage mechanics of bone tissue54Machine Learning‐Based and Experimentally Validated Optimal Adhesive Fibril Designs54Words to Matter: De novo Architected Materials Design Using Transformer Neural Networks40Machine Learning‐Driven Biomaterials Evolution299ColGen: An end-to-end deep learning model to predict thermal stability of de novo collagen sequences36Biomimicry for natural and synthetic composites and use of machine learning in hierarchical design2Physically soft magnetic films and devices: fabrication, properties, printability, and applications7Rapid prediction of protein natural frequencies using graph neural networks30End-to-End Deep Learning Model to Predict and Design Secondary Structure Content of Structural Proteins51Deep learning based design of porous graphene for enhanced mechanical resilience22The Synergistic Role of Additive Manufacturing and Artificial Intelligence for the Design of New Advanced Intelligent Systems41PRESTO: Rapid protein mechanical strength prediction with an end-to-end deep learning model17The philosophy of extreme biomimetics26Fracture at the two-dimensional limit16A State-of-the-Art Review on Machine Learning-Based Multiscale Modeling, Simulation, Homogenization and Design of Materials222Hierarchical Multiresolution Design of Bioinspired Structural Composites Using Progressive Reinforcement Learning55A Review of Performance Prediction Based on Machine Learning in Materials Science61Linking atomic structural defects to mesoscale properties in crystalline solids using graph neural networks47Al-Sn-Al Bonding Strength Investigation Based on Deep Learning Model3Fractal Geometry and Convolutional Neural Networks for the Characterization of Thermal Shock Resistances of Ultra-High Temperature Ceramics4Data-Driven Machine Learning to Predict Mechanical Properties of Monolayer Transition-Metal Dichalcogenides for Applications in Flexible Electronics38Prediction of punching shear strength of slab-column connections: A comprehensive evaluation of machine learning and deep learning based approaches31In silico approaches for polymeric nanocomposites0Predicting the Mechanical Response Profile of Porous Materials Via Microstructure-Informed Neural Networks1Designing Bioinspired Composite Structures via Genetic Algorithm and Conditional Variational Autoencoder29Advanced ocean wave energy harvesting: current progress and future trends54Fill in the Blank: Transferrable Deep Learning Approaches to Recover Missing Physical Field Information33Accelerated Design of Architected Materials with Multifidelity Bayesian Optimization6Reinforcement learning design framework for nacre-like structures optimized for pre-existing crack resistance12Deep Learning Model to Predict Ice Crystal Growth17An interpretable deep learning approach for designing nanoporous silicon nitride membranes with tunable mechanical properties21Banana/ coir biofibers and carbon/innegra fabrics and BN/MWCNT nanoparticles reinforced UV resistant polyester hybrid composites14Prediction of tensile performance for 3D printed photopolymer gyroid lattices using structural porosity, base material properties, and machine learning40Predicting the temperature field of composite materials under a heat source using deep learning16A short review on machine learning for the purpose of optimizing and predicting the properties of polymeric nanocomposites21Prediction of complex strain fields in concrete using a deep learning approach12Perspective: Machine Learning in Design for 3D/4D Printing60Deep learning-based detection method for analysis of high-pressure hydrogen induced damage in acrylonitrile butadiene rubber for hydrogen mobility10Generating three-dimensional bioinspired microstructures using transformer-based generative adversarial network15Current Advancement and Future Prospects: Biomedical Nanoengineering3Machine learning-based morphological and mechanical prediction of kirigami-inspired active composites47Artificial Intelligence - Insights into the Mechanics of Biomaterials: Predicting the Compressive Load of Composite Sandwich Structures3Deep learning in computational mechanics: a review162A bio-based nanofibre hydrogel filter for sustainable water purification111Optimization of grid composite configuration to maximize toughness using integrated hierarchical deep neural network and genetic algorithm13Prediction and optimization of global temperature field of composite materials under multiple heat sources10Bottom‐Up Film‐to‐Bulk Assembly Toward Bioinspired Bulk Structural Nanocomposites43Machine Learning in Biomaterials, Biomechanics/Mechanobiology, and Biofabrication: State of the Art and Perspective60High fidelity FEM based on deep learning for arbitrary composite material structure10An automated design framework for composite mechanical metamaterials and its application to 2D pentamode materials11Artificial Intelligence in Biomaterials: A Comprehensive Review120Equivalent Morphology Concept in Composite Materials Using Machine Learning and Genetic Algorithm Coupling3Data-driven bio-mimetic composite design: Direct prediction of stress–strain curves from structures using cGANs22FE-based machine learning model for predictive damage assessment in bonded composite joints via acoustic emission29Optimized Design of Mechanical Cantilever Structure Parameters and Stress Field Inversion Study Based on Deep Neural Network0Machine learning-based multi-objective optimization of thermo-mechanical field of anisotropic plates11Bioinspired Materials for Sustainable Technological Development0Methodological Overview of Deep Learning in Computational Mechanics1Prospects of Machine Learning-Driven Atomistic Simulations: A Review0State of the art in machine learning for the purpose of optimizing and predicting the properties of polymeric nanocomposites2Neuro-symbolic artificial intelligence in accelerated design for 4D printing: Status, challenges, and perspectives25Evaluation of tensile properties of 3D-printed lattice composites: Experimental and machine learning-based predictive modelling26Revolutionizing the Future of Smart Materials: A Review of 4D Printing, Design, Optimization, and Machine Learning Integration39Prediction of damage evolution in CMCs considering the real microstructures through a deep-learning scheme9Artificial Intelligence Driven Applications in Analytical Chemistry, Drug Discovery, and Food Science: Advancements, Outlook, and Challenges17Recent Advances in Molybdenum Disulfide (MoS 2 ) and MXene‐based Heterostructures for Photovoltaic and Water Splitting Applications: A Review31Recent advances in machine learning guided mechanical properties prediction and design of two-dimensional materials16Hierarchical design of mechanical metamaterials: an application on pentamode-like structures18Predicting compressive stress-strain behavior of elasto-plastic porous media via morphology-informed neural networks5Optimizing Bioinspired Composite Materials Using Deep Q‐Network Reinforcement Learning and Finite Element Methods4Advancing cancer diagnosis through surface plasmon resonance detection of extracellular vesicles7A review of artificial intelligence (AI)-based applications to nanocomposites17Recent Progress of Artificial Intelligence Application in Polymer Materials54A comprehensive Ai-driven framework for predictive design of grained materials: Case study on nanoglass4Artificial intelligence-assisted next-generation biomaterials: From design and preparation to medical applications29Tailoring Frictional Properties of Surfaces Using Diffusion Models0Multidisciplinary approaches to microstructural impacts on strength and fracture: from fundamental equations to machine learning techniques2Artificial intelligence-driven approaches for the rational design of peptides with predictable aggregation propensity9Feasible-machining-area mapping through AI for bandsawing1The Future of Polymer Science: Artificial Intelligence and Beyond0Machine Learning Meets Fracture Mechanics: What Can We Learn?1Artificial intelligence-enabled material design for automated manufacturing of tailored composites – a review5Modelling and visualisation of laser micromachining of biopolymers using Fibonacci-inspired neural networks0Intelligent additive manufacturing using AI and machine learning0

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  2. Nanoengineering in biomedicine: Current development and future perspectives

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  3. Sonification based de novo protein design using artificial intelligence, structure prediction, and analysis using molecular modeling

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  4. Designing an Adhesive Pillar Shape with Deep Learning-Based Optimization

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  5. Using Deep Learning to Predict Fracture Patterns in Crystalline Solids

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  6. Digital and experimental synergies to design high-heeled shoes

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  9. WebNet: A biomateriomic three-dimensional spider web neural net

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  10. Hydration-induced reversible deformation of biological materials

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