<p>Nature's ingenuity, from nacre's brick-and-mortar toughness to the adaptive wrinkling of plant cuticles, has long inspired materials design. Historically, turning such inspiration into practical materials and functional systems demanded exhaustive, iterative cycles of empirical trial and error. Today, computational modelling and simulation have shifted this paradigm, moving bio-inspired engineering from retrospective mimicry to genuinely predictive, forward-looking design. Atomistic simulations increasingly predict protein self-assembly and molecular interactions; phase-field and finite element methods resolve intricate microstructural evolution; and machine learning algorithms rapidly explore vast design spaces, uncovering structural architectures that are beyond human intuition. Computational methodologies have thus become powerful engines of discovery, actively driving innovation rather than merely facilitating it.</p><p>Hybrid computational strategies now represent the state of the art, in which physics-based models interact seamlessly with data-driven surrogates. These integrated approaches are orchestrated within dynamic digital twins that continuously refine predictions through real-time experimental feedback. Multiscale workflows bridge molecular, mesoscopic, and structural scales to enable rapid virtual prototyping and design optimisation (<b>Figure</b> 1).</p><p>Nevertheless, these advances come with significant challenges. First, achieving seamless multiscale integration across quantum, molecular, and continuum levels remains elusive. Second, ensuring robust uncertainty quantification and interpretability in machine learning models is essential for building trust, especially when applying these models to novel and previously untested scenarios. Third, computational efficiency must keep pace with growing ambitions, which in turn demands the adoption of GPU-accelerated computing, sophisticated reduced-order models, and intelligent surrogate techniques. Addressing these challenges requires unprecedented collaboration among materials scientists, biologists, mechanical engineers, data scientists, and product designers.</p><p>The articles featured in this Special Section showcase the diversity and maturity of computational approaches shaping contemporary bio-inspired materials research.</p><p>A perspective on machine learning for disordered materials outlines opportunities and challenges for extracting structure-property relations in intrinsically non-periodic systems [202402486], while an overview of RNA molecular dynamics explores multiscale simulation methods and their relevance to the development of RNA-based materials and nanostructures [202402289]. A study of fungi-inspired networks combines microscopy, compression/nanoindentation, and stochastic 3D-Voronoi finite element models to show how filament orientation alone can tune stiffness in monomitic versus dimitic mushroom networks, pointing to imaging-informed, simulation-assisted d
{"title":"From Biomimicry to Autonomous Design: The Computational Revolution in Bio-Inspired Materials","authors":"Payam Khazaeinejad","doi":"10.1002/adem.202502110","DOIUrl":"https://doi.org/10.1002/adem.202502110","url":null,"abstract":"<p>Nature's ingenuity, from nacre's brick-and-mortar toughness to the adaptive wrinkling of plant cuticles, has long inspired materials design. Historically, turning such inspiration into practical materials and functional systems demanded exhaustive, iterative cycles of empirical trial and error. Today, computational modelling and simulation have shifted this paradigm, moving bio-inspired engineering from retrospective mimicry to genuinely predictive, forward-looking design. Atomistic simulations increasingly predict protein self-assembly and molecular interactions; phase-field and finite element methods resolve intricate microstructural evolution; and machine learning algorithms rapidly explore vast design spaces, uncovering structural architectures that are beyond human intuition. Computational methodologies have thus become powerful engines of discovery, actively driving innovation rather than merely facilitating it.</p><p>Hybrid computational strategies now represent the state of the art, in which physics-based models interact seamlessly with data-driven surrogates. These integrated approaches are orchestrated within dynamic digital twins that continuously refine predictions through real-time experimental feedback. Multiscale workflows bridge molecular, mesoscopic, and structural scales to enable rapid virtual prototyping and design optimisation (<b>Figure</b> 1).</p><p>Nevertheless, these advances come with significant challenges. First, achieving seamless multiscale integration across quantum, molecular, and continuum levels remains elusive. Second, ensuring robust uncertainty quantification and interpretability in machine learning models is essential for building trust, especially when applying these models to novel and previously untested scenarios. Third, computational efficiency must keep pace with growing ambitions, which in turn demands the adoption of GPU-accelerated computing, sophisticated reduced-order models, and intelligent surrogate techniques. Addressing these challenges requires unprecedented collaboration among materials scientists, biologists, mechanical engineers, data scientists, and product designers.</p><p>The articles featured in this Special Section showcase the diversity and maturity of computational approaches shaping contemporary bio-inspired materials research.</p><p>A perspective on machine learning for disordered materials outlines opportunities and challenges for extracting structure-property relations in intrinsically non-periodic systems [202402486], while an overview of RNA molecular dynamics explores multiscale simulation methods and their relevance to the development of RNA-based materials and nanostructures [202402289]. A study of fungi-inspired networks combines microscopy, compression/nanoindentation, and stochastic 3D-Voronoi finite element models to show how filament orientation alone can tune stiffness in monomitic versus dimitic mushroom networks, pointing to imaging-informed, simulation-assisted d","PeriodicalId":7275,"journal":{"name":"Advanced Engineering Materials","volume":"27 22","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://advanced.onlinelibrary.wiley.com/doi/epdf/10.1002/adem.202502110","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145547257","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}