ArticleAdvanced materials (Deerfield Beach, Fla.)2026
Machine Learning-Assisted Bio-Interfacial Engineering Resolves Structural-Functional Conflicts in Nanocomposites.
Article in Advanced materials (Deerfield Beach, Fla.), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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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/m
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