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

2025-05-22

AI Summary

The AI-driven materials generation field lacks systematic surveys and standardized evaluation frameworks, hindering methodological comparability and reproducibility. To address this, we propose the first comprehensive, multi-dimensional taxonomy specifically tailored for materials generation—categorizing crystal structure representations (e.g., atomic coordinates, crystal graphs, SMILES), generative modeling paradigms (including graph neural networks, variational autoencoders, diffusion models, reinforcement learning, and multimodal representation learning), and standardized evaluation protocols. We systematically review over 100 state-of-the-art studies, synthesize widely adopted quantitative metrics (e.g., validity, uniqueness, novelty, reconstruction fidelity, and property-guided success rate), and curate the largest open-source resource repository—Awesome-AI-for-Materials-Generation—on GitHub. This survey fills a critical gap in the literature, providing researchers with an authoritative reference for model design, benchmarking, and reproducible scientific inquiry in generative materials discovery.

Technology Category

• Machine Learning: Deep Generative Models & Autoencoders
• Natural Language Processing: Generation
• Cognitive Modeling & Cognitive Systems: Computational Creativity

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

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.

Problem

• Survey AI-driven materials generation for tailored properties
• Organize materials types and crystalline representations systematically
• Summarize AI methods, metrics, datasets for materials discovery

Innovation

• AI-driven generative models for materials design
• Systematic survey of crystalline materials representations
• Open-source benchmark datasets and evaluation metrics