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Machine Learning-Assisted Bio-Interfacial Engineering Resolves Structural-Functional Conflicts in Nanocomposites
Hao Wang1, Xianfeng Chen2, Peiyao Yan1
1Department of Materials Science and Engineering, National University of Singapore, Singapore, Singapore.
Advanced Materials (Deerfield Beach, Fla.)
|March 25, 2026
Summary
Machine learning accelerates the discovery of advanced nanocomposites. This AI-driven approach significantly reduces experiments, costs, and time, enabling the creation of strong, tough, and multifunctional materials like mycelium-graphene.
Area of Science:
- Materials Science
- Nanotechnology
- Artificial Intelligence
Background:
- Developing high-performance nanocomposites faces challenges due to complex trade-offs in material properties.
- Conventional methods are inefficient for exploring vast composition-processing spaces.
Purpose of the Study:
- To introduce a machine learning-assisted framework for efficient nanocomposite design.
- To accelerate the discovery of materials with enhanced strength, toughness, and multifunctionality.
Main Methods:
- Utilized Gaussian-process surrogates, Pareto set learning, and active learning for design space exploration.
- Integrated machine learning with experimental validation to optimize nanocomposite properties.
- Applied the framework to mycelium-graphene and MXene systems.
Main Results:
- Achieved significant reductions in experimental count (74-85%), project duration, and cost.
- Developed mycelium-graphene composites with high strength (>58 MPa), toughness (>6 MJ/m³), and novel functionalities.
- Demonstrated enhanced resilience and electromagnetic interference shielding (>40 dB) in MXene composites.
Conclusions:
- The machine learning framework enables rapid, cost-effective discovery of advanced nanocomposites.
- The developed materials exhibit superior mechanical properties and unlock new applications.
- This approach offers a scalable and sustainable paradigm for future material innovation.
