Authors: P. Khazaeinejad
Source: Advanced Engineering Materials (article, 2025-11-01)
Field: Materials Science — Materials Chemistry

From Biomimicry to Autonomous Design: The Computational Revolution in Bio‐Inspired Materials 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. 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 (Figure 1). 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. The articles featured in this Special Section showcase the diversity and maturity of computational approaches shaping contemporary bio-inspired materials research. 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 design of cellular architectures [202402949]. In nacre-like ceramics, single lap joint experiments interrogate how the soft mortar volume fraction governs interfacial mechanics and energy dissipation, highlighting interface-sensitive routes to toughness [202500297]. A machine learning perspective on bio-inspired ceramics maps promising paradigms and future directions for data-driven inverse design across the field [202400792]. For architected structures, supervised deep feed-forward networks accelerate the evaluation and classification of 3D printed Y-core sandwich beams under static and dynamic compression, serving as predictive surrogates for design studies [202402157]. Complementing this, a reinforcement learning framework (Deep Q-Network) coupled with finite elements autonomously discovers improved microstructures in bio-inspired composites [202402807]. A multiphysics hydrogel-based sensor model linking diffusion, swelling, and electrostatics predicts real-time ammonium sensing with an approximately 20% capacitance change within about 200 s at roughly 3 μM, and a plateau response above 1 mM, underscoring relevance for environmental monitoring [202400314]. Wrinkling-activated soft-layered composites exhibit multilinear stress-strain behaviour and enhanced strain-energy storage in nonlinear shell simulations, suggesting protective and energy-absorbing applications without prescribing a fixed improvement factor [202400750]. Moreover, a geometric bifurcation framework provided predictive insights into dual order-disorder transitions, extending the computational repertoire toward predictive phase-transition design [202402724]. Three converging trends signal a new era of accelerated progress. First, closed-loop digital twins will sharply shorten discovery and validation cycles by integrating real-time experimental measurements with computational models. Second, reward-driven “materials programming”, which leverages reinforcement learning strategies, will enable computational frameworks to autonomously discover structural principles inspired by nature. Third, embracing biological variability through probabilistic computational approaches will allow designers to harness natural heterogeneity as an asset rather than a constraint. Computationally guided, bio-inspired innovations promise transformative impact far beyond the field of materials engineering. These advances address global challenges in areas such as sustainable manufacturing, advanced healthcare technologies, autonomous robotics, and space exploration by delivering materials with unprecedented performance and adaptability. In contrast to the empirical methodologies that dominated biomimetic research for decades, modern computational approaches deliver orders-of-magnitude gains in efficiency, accuracy, and scalability, thereby fundamentally reshaping the innovation cycle. As computational bio-inspired design moves toward greater autonomy, it is essential to embed ethical considerations, transparent data practices, and societal acceptance into development from the outset. Equally critical is strengthening the synergy between computational predictions and experimental validation. A tightly integrated workflow will both improve predictive accuracy and accelerate the translation of computational breakthroughs into industrial and societal applications. Emerging technologies, such as quantum computing, neuromorphic architectures, and physics-informed neural networks, offer significant potential to expand multiscale capabilities, enhance computational efficiency, and increase predictive realism. Collectively, the contributions in this Special Section provide a robust foundation for exploring these opportunities. By highlighting the richness and maturity of computational approaches and situating them within broader societal, historical, ethical, and technological contexts, we hope to catalyse deeper interdisciplinary collaboration. Such efforts promise to accelerate the development of bio-inspired materials and systems whose functional capabilities may soon rival, or even surpass, those found in nature. Payam Khazaeinejad is an Associate Professor in Solid Mechanics at Kingston University London. He received his PhD in Engineering from the University of Edinburgh. His research interests focus on computational design as a transformative approach in materials science and structural engineering. Grounded in solid mechanics and advanced simulation, his work explores bio-inspired design principles to engineer materials and systems with extraordinary properties and adaptability, from microstructural architectures to full-scale applications. Workflow ; Computational model ; Biomimetics ; Emulation ; Interpretability ; Surrogate model ; Pace ; Lead (geology) ; Artificial life Machine Learning in Materials Science ; Calcium Carbonate Crystallization and Inhibition ; Advanced Materials and Mechanics