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RESEARCH PAPER · Materials Horizons · 2020

Artificial intelligence and machine learning in design of mechanical materials

Kai Guo, Zhenze Yang, Chi‐Hua Yu, Markus J. Buehler

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Artificial intelligence and machine learning in design of mechanical materialsOriginalPhysics-Informed Neural Networks with Hard Constraints for Inverse Design771Physics-Informed Neural Networks with Hard Constraints for Inverse Design43A Review of Application of Machine Learning in Design, Synthesis, and Characterization of Metal Matrix Composites: Current Status and Emerging Applications76End-to-end deep learning method to predict complete strain and stress tensors for complex hierarchical composite microstructures147Knowledge extraction and transfer in data-driven fracture mechanics67Accelerating the Selection of Covalent Organic Frameworks with Automated Machine Learning55Emergence of machine learning in the development of high entropy alloy and their prospects in advanced engineering applications54Applications of computational chemistry, artificial intelligence, and machine learning in aquatic chemistry research91Deep learning approach to assess damage mechanics of bone tissue54Analytical model for the prediction of permeability of triply periodic minimal surfaces98Prediction and validation of the transverse mechanical behavior of unidirectional composites considering interfacial debonding through convolutional neural networks93Application of Artificial Neural Networks for Prediction of Mechanical Properties of CNT/CNF Reinforced Concrete39Materials Informatics for Mechanical Deformation: A Review of Applications and Challenges67Words to Matter: De novo Architected Materials Design Using Transformer Neural Networks40The shape – morphing performance of magnetoactive soft materials185A deep learning approach for efficient topology optimization based on the element removal strategy39Machine‐Learning Microstructure for Inverse Material Design117Encoding and exploring latent design space of optimal material structures via a VAE-LSTM model53Machine learning predicts fretting and fatigue key mechanical properties61Deep Learning Unlocks X‐ray Microtomography Segmentation of Multiclass Microdamage in Heterogeneous Materials41Machine Learning‐Evolutionary Algorithm Enabled Design for 4D‐Printed Active Composite Structures143A deep learning augmented genetic algorithm approach to polycrystalline 2D material fracture discovery and design44From Drug Molecules to Thermoset Shape Memory Polymers: A Machine Learning Approach50Predicting output performance of triboelectric nanogenerators using deep learning model58Dispersion relation prediction and structure inverse design of elastic metamaterials via deep learning95Physics-informed neural networks based on adaptive weighted loss functions for Hamilton-Jacobi equations39Machine Learning–Assisted Design of Material Properties80Generative design, manufacturing, and molecular modeling of 3D architected materials based on natural language input62Mechanical properties and healing efficiency of 3D-printed ABS vascular based self-healing cementitious composite: Experiments and modelling41Computational Acceleration of Topology Optimization Using Parallel Computing and Machine Learning Methods – Analysis of Research Trends37Deep learning accelerated prediction of the permeability of fibrous microstructures57End-to-end prediction of multimaterial stress fields and fracture patterns using cycle-consistent adversarial and transformer neural networks50FieldPerceiver: Domain agnostic transformer model to predict multiscale physical fields and nonlinear material properties through neural ologs52Deep learning for the rare-event rational design of 3D printed multi-material mechanical metamaterials76A review on Machine learning aspect in physics and mechanics of glasses44Nacre-like composites with superior specific damping performance50Overview of Methods and Software for the Design of Functionally Graded Lattice Structures73Hierarchical 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 networks47Microstructural image based convolutional neural networks for efficient prediction of full-field stress maps in short fiber polymer composites53Multifunctional 1D Nanostructures toward Future Batteries: A Comprehensive Review91On the use of artificial neural networks in topology optimisation199Data-Driven Machine Learning to Predict Mechanical Properties of Monolayer Transition-Metal Dichalcogenides for Applications in Flexible Electronics38Finite Element-Based Machine Learning Model for Predicting the Mechanical Properties of Composite Hydrogels36Recent developments in the application of machine-learning towards accelerated predictive multiscale design and additive manufacturing133Inverse design of truss lattice materials with superior buckling resistance111Optimization of vascular structure of self-healing concrete using deep neural network 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identifying material properties in solid mechanics60Recent Advances and Applications of Machine Learning in Experimental Solid Mechanics: A Review157Can Artificial Intelligence Accelerate Fluid Mechanics Research?59Deep learning aided inverse design of the buckling-guided assembly for 3D frame structures57Artificial intelligence enhanced sensors - enabling technologies to next-generation healthcare and biomedical platform290Artificial Intelligence in the Design of Innovative Metamaterials: A Comprehensive Review44Untapped Potential of Deep Eutectic Solvents for the Synthesis of Bioinspired Inorganic–Organic Materials58Unleashing the Power of Artificial Intelligence in Materials Design179Application of Machine Learning in Material Synthesis and Property Prediction166Artificial Intelligence in Predicting Mechanical Properties of Composite Materials249Inverse design of multi-material gyroid structures made by additive manufacturing55Recent progress on noble-free substrates for surface-enhanced Raman spectroscopy analysis75Contemporary Role and Applications of Artificial Intelligence in Dentistry121Rapid design of top-performing metal-organic frameworks with qualitative representations of building blocks36Perspective: Machine Learning in Design for 3D/4D Printing60MeLM, a generative pretrained language modeling framework that solves forward and inverse mechanics problems66Prediction of shear strength in UHPC beams using machine learning-based models and SHAP interpretation123Neural Networks for Constitutive Modeling: From Universal Function Approximators to Advanced Models and the Integration of Physics100A machine learning approach for accelerated design of magnesium alloys. 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advances of stimuli-responsive catalytic systems for biomedical applications40Recent advances of artificial intelligence in quantitative analysis of food quality and safety indicators: A review45Advancement of capacitive deionization propelled by machine learning approach42Cephalo: Multi‐Modal Vision‐Language Models for Bio‐Inspired Materials Analysis and Design38State-of-art review on the process-structure-properties-performance linkage in wire arc additive manufacturing95Physics-informed Neural Networks (PINN) for computational solid mechanics: Numerical frameworks and applications335Recent Research on the Optimization of Interfacial Structure and Interfacial Interaction Mechanisms of Metal Matrix Composites: A Review43The application and impact of artificial intelligence technology in graphic design: A critical interpretive synthesis40A Review on Data-Driven Constitutive Laws for Solids142How does artificial intelligence affect manufacturing firms' energy intensity?93AI in Context: Harnessing Domain Knowledge for Smarter Machine Learning38Machine learning-based constitutive modelling for material non-linearity: A review65SciAgents: Automating Scientific Discovery Through Bioinspired Multi‐Agent Intelligent Graph Reasoning146A review of machine learning applications in polymer composites: advancements, challenges, and future prospects135Accelerating battery innovation: AI-powered molecular discovery58AI-driven advances in metal–organic frameworks: from data to design and applications47Cuproptosis: mechanisms and nanotherapeutic strategies in cancer and beyond90From synthesis to properties: expanding the horizons of machine learning in nanomaterials research39Artificial intelligence interventions in 2D MXenes-based photocatalytic applications38Automating alloy design and discovery with physics-aware multimodal multiagent AI71Harnessing artificial intelligence to fill global shortfalls in biodiversity knowledge64Advances in natural fiber polymer and PLA composites 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