Updated: Nov 9, 2020
Abstract: Biomimicry, adapting and implementing nature's designs provides an adequate first-order solution to achieving superior mechanical properties. However, the design space is too vast even using biomimetic designs as prototypes for optimization. Here, we propose a new approach to design hierarchical materials using machine learning, trained with a database of hundreds of thousands of structures from finite element analysis, together with a self-learning algorithm for discovering high-performing materials where inferior designs are phased out for superior candidates. Results show that our approach can create microstructural patterns that lead to tougher and stronger materials, which are validated through additive manufacturing and testing. We further show that machine learning can be used as an alternative method of coarse-graining – analyzing and designing materials without the use of full microstructural data. This novel paradigm of smart additive manufacturing can aid in the discovery and fabrication of new material designs boasting orders of magnitude increase in computational efficacy over conventional methods.
Full paper: GX Gu, C-T Chen, DJ Richmond, MJ Buehler, Bioinspired hierarchical composite design using machine learning: simulation, additive manufacturing, and experiment, Materials Horizons, 2018, DOI: 10.1039/C8MH00653A
Updated: Nov 9, 2020
Abstract: Composites are widely used to create tunable materials to achieve superior mechanical properties. Brittle materials fail catastrophically in the presence of cracks. Incorporating softer constituents into brittle materials can alleviate stress concentration, leading to tougher and stronger composites. However, searching for the optimal designs of composites is extremely challenging due to the astronomical number of possible material and geometry combinations. Here, we apply machine learning to a composite system and demonstrate its capacity to accurately and efficiently predict mechanical properties including toughness and strength. The method we used incorporates machine learning techniques to generate optimal designs with orders of magnitude better than the mean properties of the input training data, and at a much lower computational cost compared to exhaustive methods. Additionally, the method can further rebuild the detailed performances of the designs, even when this information is lost in the training process. The results demonstrate the ability of machine learning to search for optimal designs with very limited training data. To demonstrate the application of machine learning to composite design, we optimize a large-scale system not tractable by an exhaustive brute force approach and show that it is a promising tool towards composite design. This work offers a new perspective in the exploration of design spaces and accelerating the discovery of new functional, customizable composites.
Full paper: GX Gu, C-T Chen, MJ Buehler, De novo composite design based on machine learning algorithm, Extreme Mechanics Letters, 2018, DOI: 10.1016/j.eml.2017.10.001
Updated: Nov 9, 2020
Abstract: Graphene and other two-dimensional materials have unique physical and chemical properties of broad relevance. It has been suggested that the transformation of these atomically planar materials to three-dimensional (3D) geometries by bending, wrinkling, or folding could significantly alter their properties and lead to novel structures and devices with compact form factors, but strategies to enable this shape change remain limited. We report a benign thermally responsive method to fold and unfold monolayer graphene into predesigned, ordered 3D structures. The methodology involves the surface functionalization of monolayer graphene using ultrathin noncovalently bonded mussel-inspired polydopamine and thermoresponsive poly(N-isopropylacrylamide) brushes. The functionalized graphene is micropatterned and self-folds into ordered 3D structures with reversible deformation under a full control by temperature. The structures are characterized using spectroscopy and microscopy, and self-folding is rationalized using a multiscale molecular dynamics model. Our work demonstrates the potential to design and fabricate ordered 3D graphene structures with predictable shape and dynamics. We highlight applicability by encapsulating live cells and creating nonlinear resistor and creased transistor devices.
Full paper: W Xu, Z Qin, C-T Chen, HR Kwag, Q Ma, A Sarkar, MJ Buehler, DH Gracias, Ultrathin thermoresponsive self-folding 3D graphene, Science Advances, 2017, DOI: 10.1126/sciadv.1701084