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Machine Learning–Assisted Bio-Interfacial Engineering Resolves Structural–Functional Conflicts in Nanocomposites
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DOI：10.1002/adma.202518806.png)
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Abstract
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Delivering nanocomposites that combine high strength, toughness, and multifunctionality remains a major challenge, as conventional trial-and-error and design-of-experiments approaches cannot efficiently resolve trade-offs in high-dimensional design spaces. We introduce a machine-learning–assisted bio-interfacial design framework integrating Gaussian-process surrogates, Pareto set learning, and active learning to explore composition–processing spaces under calibrated uncertainty. The workflow converges after nearly 60 experiments, reducing experimental count, project duration, and cost by 74%–85% relative to conventional methods, thereby accelerating design cycles and expanding Pareto coverage. Guided by this approach, we realize mycelium–graphene composites with strength >58 MPa, toughness >6 MJ/m3, and levitation >0.14 mm, showing that strength can be maintained while toughness is significantly enhanced and multifunctionality unlocked. Mechanistic analyses reveal nanosheet-pinned, hierarchically entangled interfaces where hydrogen-bonded junctions enable reversible nanosheet sliding, crack deflection, and adaptive stress transfer. These architectures impart levitation control, laser-driven actuation, and self-healing. Extension to MXene systems yields composites with enhanced resilience and electromagnetic interference shielding above 40 dB, confirming the generality of the strategy. Together, these advances define a scalable and sustainable paradigm for the accelerated discovery of robust, multifunctional nanocomposites.
Keywords:
bio-interfacial engineering
machine learning–guided optimization
multifunctional nanocomposites
strength–toughness optimization
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