New Methods of Preparing Calcium Nanomaterials as a Keystone in Biotechnology

The integration of artificial intelligence (AI) with nanomaterials science is transforming the design and development of calcium-based nanomaterials. These nanostructures— primarily composed of calcium phosphate, calcium carbonate, and calcium hydroxidehold vast potential in biomedical engineering, targeted cancer therapy, environmental remediation, and biomimetic self-assembly. Recent advances in AI, including generative models such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), as well as reinforcement learning and explainable machine learning, now enable the predictive design and data-driven optimization of nanomaterials with high precision. This review presents a structured overview of AI-enabled inverse design, synthesis pathway optimization, property prediction, and molecular simulation in the context of calcium nanomaterials. It also highlights real-world applications where AI-enhanced materials exhibit improved bioactivity, compatibility, and multifunctionality. Despite rapid progress, challenges remain in data standardization, model generalizability, and integration with experimental validation. Continued development of closed-loop AI-experimental systems and scalable design strategies will be key to realizing the full potential of intelligent calcium nanomaterials in advanced healthcare and environmental technologies.