Machine Learning-Assisted Bio-Interfacial Engineering Resolves Structural-Functional Conflicts in Nanocomposites.

Abstract

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.