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RESEARCH PAPER · Advanced Materials · 2019

Bayesian Machine Learning in Metamaterial Design: Fragile Becomes Supercompressible

Miguel Aníbal Bessa, Piotr Glowacki, Michael Houlder

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Bayesian Machine Learning in Metamaterial Design: Fragile Becomes SupercompressibleOriginalMolecular Generation Targeting Desired Electronic Properties via Deep Generative Models0Molecular generation targeting desired electronic properties via deep generative models44Highly Stretchable Bilayer Lattice Structures That Elongate via In‐Plane Deformation6The Critical Raw Materials in Cutting Tools for Machining Applications: A Review228Accelerated design and characterization of non-uniform cellular materials via a machine-learning based framework90Topology optimization of 2D structures with nonlinearities using deep learning222Inverse-designed spinodoid metamaterials443Ply-drop design of non-conventional composites using Bayesian optimization0Enhancing City Sustainability through Smart Technologies: A Framework for Automatic Pre-Emptive Action to Promote Safety and Security Using Lighting and ICT-Based Surveillance40Devising Materials Manufacturing Toward Lab‐to‐Fab Translation of Flexible Electronics109Foundations for Soft, Smart Matter by Active Mechanical Metamaterials144Evolutionary computation for design and characterization of nanoscale metastructures11A predictive deep-learning approach for homogenization of auxetic kirigami metamaterials with randomly oriented cuts15Machine learning-based design and optimization of curved beams for multistable structures and metamaterials139Ply-drop design of non-conventional laminated composites using Bayesian optimization31A semi-supervised approach to architected materials design using graph neural networks73Geometric Stability Classification: Datasets, Metamodels, and Adversarial Attacks9Hierarchical Deep Learning Neural Network (HiDeNN): An artificial intelligence (AI) framework for computational science and engineering181MAP123-EPF: A mechanistic-based data-driven approach for numerical elastoplastic modeling at finite strain39Autonomous Discovery of Battery Electrolytes with Robotic Experimentation and Machine Learning186Monolithic binary stiffness building blocks for mechanical digital machines30Meshless physics-informed deep learning method for three-dimensional solid mechanics170Data-driven design strategy in fused filament fabrication: status and opportunities37Artificial intelligence and machine learning in design of mechanical materials687The statistical finite element method (statFEM) for coherent synthesis of observation data and model predictions79Exploring the potential of transfer learning for metamodels of heterogeneous material deformation21A data driven design strategy to improve quality in additive manufacturing0Polycarbonate : antimicrobial polymer delivery, monomer synthesis, rapid polymerization and functionalization0Towards the digitalisation of porous energy materials: evolution of digital approaches for microstructural design62Digital strategies for structured and architected materials design22Sparse quantum Gaussian processes to counter the curse of dimensionality4An ensemble method for predicting the mechanical properties of strain hardening cementitious composites33Parameter optimization of bidirectional re-entrant auxetic honeycomb metamaterial based on genetic algorithm122Inverse design and experimental verification of an acoustic sink based on machine learning67Oligomodal metamaterials with multifunctional mechanics81A methodology to generate design allowables of composite laminates using machine learning112Machine Learning for Warpage Prediction of Fused Deposition Modelling Processed Parts0A neural network-aided Bayesian identification framework for multiscale modeling of nanocomposites26Knowledge extraction and transfer in data-driven fracture mechanics67Optical cloaking and invisibility: From fiction toward a technological reality38Design of Pseudo-Mechanisms and Multistable Units for Mechanical Metamaterials29Deep learning method for predicting the mechanical properties of aluminum alloys with small data sets52Deciphering controversial results of cell proliferation on TiO2 nanotubes using machine learning16Reduced Order Multiscale Simulation of Diffuse Damage in Concrete13Inverse-Designed Spinodoid Metamaterials11A fractal model of effective mechanical properties of porous composites39Data‐Driven Approaches Toward Smarter Additive Manufacturing37Energy absorption prediction and optimization of corrugation-reinforced multicell square tubes based on machine learning52TONR: An exploration for a novel way combining neural network with topology optimization105Inverted and Programmable Poynting Effects in Metamaterials3Density‐Graded Cellular Solids: Mechanics, Fabrication, and Applications104Probabilistic Modeling of Discrete Structural Response with Application to Composite Plate Penetration Models13A Bayesian surrogate constitutive model to estimate failure probability of elastomers24Microstructure–property relation and machine learning prediction of hole expansion capacity of high-strength steels17Strength through 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Enhanced Photoluminescence Quantum Yield68Machine learning constitutive models of elastomeric foams38Reduction of windturbine generated seismic noise with structural measures1Machine-learning-accelerated design of functional structural components in deep-sea soft robots21FRACTAL-BASED MODEL FOR EVALUATING THE FILTRATION EFFICIENCY OF THE NON-WOVEN FIBROUS COMPOSITES2Comment on wes-2022-30Deep auto-encoder network in predictive design of Helmholtz resonator: On-demand prediction of sound absorption peak50Reply on RC10Reply on RC10Comment on wes-2022-30The Synergistic Role of Additive Manufacturing and Artificial Intelligence for the Design of New Advanced Intelligent Systems41A porosity-based model of dynamic compaction in under-dense materials1Reply on RC20Studying Complex Evolution of Hyperelastic Materials under External Field Stimuli using Artificial Neural Networks with Spatiotemporal Features in a Small‐Scale Dataset35Data-Driven Design Support for Additively Manufactured Heating 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low-cost portable electronic devices72Metamaterial approach to mitigate ground‐borne vibrations induced by moving trains16A State-of-the-Art Review on Machine Learning-Based Multiscale Modeling, Simulation, Homogenization and Design of Materials222Overview of Methods and Software for the Design of Functionally Graded Lattice Structures73Machine learning assisted design of shape-programmable 3D kirigami metamaterials90Systematic design of Cauchy symmetric structures through Bayesian optimization59A dynamically reprogrammable surface with self-evolving shape morphing165A systematic review on data of additive manufacturing for machine learning applications: the data quality, type, preprocessing, and management70On-demand design based on deep learning and phase manipulation of all-silicon terahertz chiral metasurfaces23Prediction of sound absorption coefficient for metaporous materials with convolutional neural networks27Machine Learning of Implicit Combinatorial Rules in Mechanical Metamaterials37Modeling and Design of Zero‐Stiffness Elastomer Springs Using Machine Learning17Research progress and development trend of smart metamaterials18Inverse Design of Digital Materials Using Corrected Generative Deep Neural Network and Generative Deep Convolutional Neural Network5Simulation and learning-driven design for architected cement-based materials10Artificial Intelligence and Advanced Materials117A machine learning approach to predicting mechanical behaviour of non-rigid foldable square-twist origami30Applications in Data-Driven Additive Manufacturing1Machine Learning Techniques for Predicting Metamaterial Microwave Absorption Performance: A Comparison89Materials with a twist : Shear-induced instabilities in metamaterials0Microstructure hull and design0Numerical characterization of tissues1Additively manufactured materials and structures: A state-of-the-art review on their mechanical characteristics and energy absorption439Computational Design and Manufacturing of Sustainable Materials through First-Principles and Materiomics73Synthesis of 2-DOF Decoupled Rotation Stage with FEA-Based Neural Network7Machine learning and experiments: A synergy for the development of functional materials19Impact Performance Prediction and Optimization of a Circumferentially Corrugated Tube with Variable Wall Thickness Using Support Vector Machine14Bayesian-Optimization-Assisted Laser Reduction of Poly(acrylonitrile) for Electrochemical Applications12A machine learning-based probabilistic computational framework for uncertainty quantification of actuation of clustered tensegrity structures8Efficient multiscale modeling of heterogeneous materials using deep neural networks71Utilizing ANN for Predicting the Cauchy Stress and Lateral Stretch of Random Elastomeric Foams under Uniaxial Loading1Buckling Metamaterials for Extreme Vibration Damping86Piezoelectric Metamaterial Blood Pressure Sensor36Nanostructured surface plasmon resonance sensors: Toward narrow linewidths37A review of the multi-dimensional application of machine learning to improve the integrated intelligence of laser powder bed fusion70Design of acoustic absorbing metasurfaces using a data-driven approach24Buckling elastomeric springs and lattices for tailored energy absorption10Machine learning of evolving physics-based material models for multiscale solid mechanics32Bayesian physics-informed extreme learning machine for forward and inverse PDE problems with noisy data55Tensile Properties of 3D‐Projected 4‐Polytopes: A New Class of Mechanical Metamaterial8Locality sensitive hashing via mechanical behavior5Programmable multi-physical mechanics of mechanical metamaterials196Machine learning aided multiscale magnetostatics25Machine learning aided nanoindentation: A review of the current state and future perspectives75Multiband Metamaterial absorber with absorption prediction by assisted machine learning61Recent Advances and Applications of Machine Learning in Experimental Solid Mechanics: A Review157Post-fabrication tuning of origami-inspired mechanical metamaterials based on Tachi-Miura Polyhedron20Unleashing the Power of Artificial Intelligence in Materials Design179Bayesian optimisation of hexagonal honeycomb metamaterial31Cubically Symmetric Mechanical Metamaterials Projected from 4th-Dimensional Geometries Reveal High Specific Properties in Shear13Deep reinforcement learning for the rapid on-demand design of mechanical metamaterials with targeted nonlinear deformation responses61Rapid inverse design of metamaterials based on prescribed mechanical behavior through machine learning387Hydrodynamic metamaterials: Principles, experiments, and applications37Data-informed statistical finite element analysis of rail buckling5Disordered mechanical metamaterials118Crashworthiness behavior assessment and multi-objective optimization of horsetail-inspired sandwich tubes based on artificial neural network30Perspective: Machine Learning in Design for 3D/4D Printing60Machine learning-driven optimization design of hydrogel-based negative hydration expansion metamaterials20AI‐Based Metamaterial Design for Wearables30Deep reinforcement learning for the design of mechanical metamaterials with tunable deformation and hysteretic characteristics35Deep Learning for Size‐Agnostic Inverse Design of Random‐Network 3D Printed Mechanical Metamaterials96Experimental Validation of Reconstructed Microstructure via Deep Learning in Discontinuous Fiber Platelet Composite11

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