| Abstract | Nature has long inspired scientific and engineering advancements, particularly in the development of bioinspired ceramics. However, replicating nature's intricate structures through subtractive manufacturing techniques remains a significant challenge due to the limitations of precise and controlled material removal while maintaining structural integrity and complexity. This perspective article explores the transformative potential of machine learning (ML), particularly advancements in generative artificial intelligence (generative adversarial networks, transformer models) and multimodal learning, in accelerating the discovery of high-performance bioinspired ceramics. ML offers an avenue to optimize material behavior beyond the constraints of traditional experimental methods. Recent advancements have shown ML's effectiveness in predicting mechanical properties and refining material designs, often surpassing conventional approaches. ML excels at identifying complex relationships even with incomplete data during training. The integration of cutting-edge experimental data, cross-scale simulations, and ML facilitates high-fidelity multiscale modeling for predicting intricate phenomena like crack propagation paths in bioinspired ceramic structures. This article emphasizes the significant potential of ML to propel the field of bioinspired ceramics forward, paving the way for the discovery of ceramics with superior and tailored properties. |
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