Unleashing the Power of Artificial Intelligence in Materials Design.

The integration of artificial intelligence (AI) algorithms in materials design is revolutionizing the field of materials engineering thanks to their power to predict material properties, design de novo materials with enhanced features, and discover new mechanisms beyond intuition. In addition, they can be used to infer complex design principles and identify high-quality candidates more rapidly than trial-and-error experimentation. From this perspective, herein we describe how these tools can enable the acceleration and enrichment of each stage of the discovery cycle of novel materials with optimized properties. We begin by outlining the state-of-the-art AI models in materials design, including machine learning (ML), deep learning, and materials informatics tools. These methodologies enable the extraction of meaningful information from vast amounts of data, enabling researchers to uncover complex correlations and patterns within material properties, structures, and compositions. Next, a comprehensive overview of AI-driven materials design is provided and its potential future prospects are highlighted. By leveraging such AI algorithms, researchers can efficiently search and analyze databases containing a wide range of material properties, enabling the identification of promising candidates for specific applications. This capability has profound implications across various industries, from drug development to energy storage, where materials performance is crucial. Ultimately, AI-based approaches are poised to revolutionize our understanding and design of materials, ushering in a new era of accelerated innovation and advancement.

Computational Mechanics of Form-Fitting 3D-Printed Lattice-Based Wrist-Hand Orthosis for Motor Neuron Disease.

Motor neuron disease (MND) patients often experience hand-wrist muscle atrophy resulting in severe social consequences and hampering their daily activities. Although hand-wrist orthosis is commonly used to assist weakened muscles, its effectiveness is limited due to the rapid progression of the disease and the need for customization to suit individual patient requirements. To address these challenges, this study investigates the application of three-dimensional (3D) printing technology to design and fabricate two lattice structures inspired by silkworm cocoons, using poly-ε-caprolactone as feedstock material. Finite element method (FEM) analysis is employed to study the mechanical behavior, enabling control over the geometric configuration incorporated into the hand-wrist orthosis. Through tensile displacement and three-point bending simulations, the stress distribution is examined for both lattice geometries. Geometry-1 demonstrates anisotropic behavior, while geometry-2 exhibits no strict directional dependence due to its symmetry and uniform node positioning. Moreover, the biocompatibility of lattices with human skin fibroblasts is investigated, confirming excellent biocompatibility. Lastly, the study involves semi-structured interviews with MND patients to gather feedback and develop prototypes of form-fitting 3D-printed lattice-based hand-wrist orthosis. By utilizing 3D printing technology, this study aims to provide customized orthosis that can effectively support weakened muscles and reposition the hand for individuals with MND.