Materials Generation in the Era of Artificial Intelligence: A Comprehensive Survey

• URL: http://arxiv.org/abs/2505.16379v1
• Date: Thu, 22 May 2025 08:33:21 GMT
• Title: Materials Generation in the Era of Artificial Intelligence: A Comprehensive Survey
• Authors: Zhixun Li, Bin Cao, Rui Jiao, Liang Wang, Ding Wang, Yang Liu, Dingshuo Chen, Jia Li, Qiang Liu, Yu Rong, Liang Wang, Tong-yi Zhang, Jeffrey Xu Yu,
• Abstract summary: Materials are the foundation of modern society, underpinning advancements in energy, electronics, healthcare, transportation, and infrastructure.<n>The ability to discover and design new materials with tailored properties is critical to solving some of the most pressing global challenges.<n>Data-driven generative models provide a powerful tool for materials design by directly create novel materials that satisfy predefined property requirements.
• Score: 54.40267149907223
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Materials are the foundation of modern society, underpinning advancements in energy, electronics, healthcare, transportation, and infrastructure. The ability to discover and design new materials with tailored properties is critical to solving some of the most pressing global challenges. In recent years, the growing availability of high-quality materials data combined with rapid advances in Artificial Intelligence (AI) has opened new opportunities for accelerating materials discovery. Data-driven generative models provide a powerful tool for materials design by directly create novel materials that satisfy predefined property requirements. Despite the proliferation of related work, there remains a notable lack of up-to-date and systematic surveys in this area. To fill this gap, this paper provides a comprehensive overview of recent progress in AI-driven materials generation. We first organize various types of materials and illustrate multiple representations of crystalline materials. We then provide a detailed summary and taxonomy of current AI-driven materials generation approaches. Furthermore, we discuss the common evaluation metrics and summarize open-source codes and benchmark datasets. Finally, we conclude with potential future directions and challenges in this fast-growing field. The related sources can be found at https://github.com/ZhixunLEE/Awesome-AI-for-Materials-Generation.

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