The rapid evolution of quantum materials has opened new frontiers in next-generation electronics, energy storage, and quantum computing. However, their precise fabrication and property control remain major challenges due to complex microstructural behaviors. Additive manufacturing (AM) offers a powerful pathway to tailor material structures at the nanoscale, yet traditional AM processes often lack predictive accuracy and process optimization. The integration of artificial intelligence (AI) with AM provides transformative potential by enabling data-driven design, intelligent process control, and property prediction for quantum materials. This special issue aims to highlight cutting-edge research that leverages AI and machine learning to enhance the fabrication, performance, and functional understanding of quantum materials, bridging the gap between computational intelligence and advanced manufacturing.

The goal of this Research Topic is to advance the integration of artificial intelligence (AI) and additive manufacturing (AM) for the precise fabrication and intelligent property prediction of quantum materials. It seeks to provide a collaborative platform for researchers, scientists, and industry experts to present innovative approaches that combine data-driven modeling, machine learning algorithms, and process optimization for the development of next-generation quantum materials. This collection aims to promote research that enhances the accuracy, scalability, and reproducibility of quantum material fabrication through AI-assisted design frameworks and predictive analytics. Emphasis will be placed on studies that establish correlations between processing parameters, microstructural evolution, and functional properties. By bridging materials science, quantum physics, and intelligent manufacturing, this special issue aspires to accelerate the discovery, customization, and application of quantum materials in advanced technologies such as quantum computing, photonics, and energy systems.

This Research Topic invites original research articles, reviews, and short communications focusing on the application of artificial intelligence (AI) and additive manufacturing (AM) for the design, fabrication, and property prediction of quantum materials. The issue encourages submissions that demonstrate AI-enabled innovations in fabrication accuracy, material optimization, and performance modeling. Studies that incorporate experimental validation, digital simulations, or interdisciplinary frameworks bridging materials science and intelligent manufacturing are particularly encouraged. Authors are invited to contribute works that advance AI-assisted process control, data-driven material design, or predictive modeling relevant to quantum materials and their emerging applications in computing, sensing, and advanced electronic systems. All submissions should exhibit technical depth, originality, and clear alignment with AI-enhanced additive manufacturing.

This Research Topic welcomes contributions addressing the intersection of AI, additive manufacturing, and quantum materials, including (but not limited to):

• Machine learning and deep learning algorithms for optimizing additive manufacturing parameters and process control.

• Predictive modeling and intelligent property estimation for quantum and other advanced materials.

• Digital twin frameworks and smart control systems to enhance process accuracy and reproducibility.

• Data-driven approaches for defect detection, monitoring, and quality assurance.

• Integration of AI and quantum computing for accelerated material discovery and performance prediction.

Submissions highlighting experimental validation, simulation-driven insights, or real-world industrial applications are strongly encouraged.

Are you interested in contributing a manuscript to this Research Topic?

AI-Enhanced Additive Manufacturing for the Fabrication and Property Prediction of Quantum Materials