Deep Learning in Mechanical Metamaterials: From Prediction and Generation to Inverse Design

Advanced Materials, Journal Year: 2023, Volume and Issue: 35(45)

Published: June 19, 2023

Abstract Mechanical metamaterials are meticulously designed structures with exceptional mechanical properties determined by their microstructures and constituent materials. Tailoring material geometric distribution unlocks the potential to achieve unprecedented bulk functions. However, current metamaterial design considerably relies on experienced designers' inspiration through trial error, while investigating responses entails time‐consuming testing or computationally expensive simulations. Nevertheless, recent advancements in deep learning have revolutionized process of metamaterials, enabling property prediction geometry generation without prior knowledge. Furthermore, generative models can transform conventional forward into inverse design. Many studies implementation highly specialized, pros cons may not be immediately evident. This critical review provides a comprehensive overview capabilities prediction, generation, metamaterials. Additionally, this highlights leveraging create universally applicable datasets, intelligently intelligence. article is expected valuable only researchers working but also those field materials informatics.

Language: Английский

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Inverse Design of Energy‐Absorbing Metamaterials by Topology Optimization

Advanced Science, Journal Year: 2022, Volume and Issue: 10(4)

Published: Dec. 11, 2022

Compared with the forward design method through control of geometric parameters and material types, inverse based on target stress-strain curve is helpful for discovery new structures. This study proposes an optimization strategy mechanical metamaterials a genetic algorithm establishes topology energy-absorbing structures desired curves. A series structural mutation algorithms design-domain-independent mesh generation are developed to improve efficiency finite element analysis iteration. The realizes ideal structures, which verified by additive manufacturing experimental characterization. error between designed structure less than 5%, densification strain reaches 0.6. Furthermore, special attention paid passive pedestrian protection occupant protection, reasonable solution given multiplatform structure. proposed framework provides path elastic-plastic large deformation problem that unable be resolved using classical gradient or algorithms, simplifies process metamaterials.

Language: Английский

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Data‐Driven Design for Metamaterials and Multiscale Systems: A Review

Advanced Materials, Journal Year: 2023, Volume and Issue: 36(8)

Published: Dec. 5, 2023

Abstract Metamaterials are artificial materials designed to exhibit effective material parameters that go beyond those found in nature. Composed of unit cells with rich designability assembled into multiscale systems, they hold great promise for realizing next‐generation devices exceptional, often exotic, functionalities. However, the vast design space and intricate structure–property relationships pose significant challenges their design. A compelling paradigm could bring full potential metamaterials fruition is emerging: data‐driven This review provides a holistic overview this rapidly evolving field, emphasizing general methodology instead specific domains deployment contexts. Existing research organized modules, encompassing data acquisition, machine learning‐based cell design, optimization. The approaches further categorized within each module based on shared principles, analyze compare strengths applicability, explore connections between different identify open questions opportunities.

Language: Английский

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Self-enhancing sono-inks enable deep-penetration acoustic volumetric printing

Science, Journal Year: 2023, Volume and Issue: 382(6675), P. 1148 - 1155

Published: Dec. 7, 2023

Volumetric printing, an emerging additive manufacturing technique, builds objects with enhanced printing speed and surface quality by forgoing the stepwise ink-renewal step. Existing volumetric techniques almost exclusively rely on light energy to trigger photopolymerization in transparent inks, limiting material choices build sizes. We report a self-enhancing sonicated ink (or sono-ink) design corresponding focused-ultrasound writing technique for deep-penetration acoustic (DAVP). used experiments modeling study frequency scanning rate-dependent behaviors. DAVP achieves key features of low streaming, rapid sonothermal polymerization, large depth, enabling hydrogels nanocomposites various shapes regardless their optical properties. also allows at centimeter depths through biological tissues, paving way toward minimally invasive medicine.

Language: Английский

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Disordered mechanical metamaterials

Nature Reviews Physics, Journal Year: 2023, Volume and Issue: 5(11), P. 679 - 688

Published: Sept. 29, 2023

Language: Английский

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Inverse design of phononic meta-structured materials

Materials Today, Journal Year: 2024, Volume and Issue: 80, P. 824 - 855

Published: Oct. 4, 2024

Language: Английский

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Aperiodicity is all you need: Aperiodic monotiles for high-performance composites

Materials Today, Journal Year: 2024, Volume and Issue: 73, P. 1 - 8

Published: Feb. 2, 2024

Language: Английский

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3D polycatenated architected materials

Science, Journal Year: 2025, Volume and Issue: 387(6731), P. 269 - 277

Published: Jan. 16, 2025

Architected materials derive their properties from the geometric arrangement of internal structural elements. Their designs rely on continuous networks members to control global mechanical behavior bulk. In this study, we introduce a class that consist discrete concatenated rings or cage particles interlocked in three-dimensional networks, forming polycatenated architected (PAMs). We propose general design framework translates arbitrary crystalline into particle concatenations and geometries. response small external loads, PAMs behave like non-Newtonian fluids, showing both shear-thinning shear-thickening responses, which can be controlled by catenation topologies. At larger strains, lattices foams, with nonlinear stress-strain relation. microscale, demonstrate change shapes applied electrostatic charges. The distinctive pave path for developing stimuli-responsive materials, energy-absorbing systems, morphing architectures.

Language: Английский

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Generative deep learning for designing irregular metamaterials with programmable nonlinear mechanical responses

International Journal of Mechanical Sciences, Journal Year: 2025, Volume and Issue: unknown, P. 110123 - 110123

Published: March 1, 2025

Language: Английский

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A deep learning approach for inverse design of gradient mechanical metamaterials

International Journal of Mechanical Sciences, Journal Year: 2022, Volume and Issue: 240, P. 107920 - 107920

Published: Nov. 7, 2022

Language: Английский

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