用AI生成模型逆向设计新材料，加速可持续发展领域创新。
Artificial Intelligence and Generative Models for Materials Discovery -- A Review
- 基于AI生成模型实现逆向材料设计，利用多种材料表征方式
- 成功应用于催化剂、半导体等新材料设计，提升发现效率
- 解决数据少、难合成等难题，适合材料与AI交叉研究者
高通量实验工具、机器学习方法和开放材料数据库正深刻改变新材料发现方式。从过去的实验驱动转向人工智能驱动，实现了根据目标性能逆向设计新材料的能力。本文综述了适用于材料发现的AI生成模型原理，包括多种材料表示方法。重点介绍生成模型在新型催化剂、半导体、聚合物和晶体设计中的应用，并讨论数据稀缺、计算成本、可解释性、可合成性及数据集偏差等挑战。同时探讨新兴解决方案，如多模态模型、物理信息架构和闭环发现系统，旨在为致力于利用AI推动可持续、医疗和能源创新的科研人员提供洞见。
原文摘要 · Abstract (English)
High throughput experimentation tools, machine learning (ML) methods, and open material databases are radically changing the way new materials are discovered. From the experimentally driven approach in the past, we are moving quickly towards the artificial intelligence (AI) driven approach, realizing the 'inverse design' capabilities that allow the discovery of new materials given the desired properties. This review aims to discuss different principles of AI-driven generative models that are applicable for materials discovery, including different materials representations available for this purpose. We will also highlight specific applications of generative models in designing new catalysts, semiconductors, polymers, or crystals while addressing challenges such as data scarcity, computational cost, interpretability, synthesizability, and dataset biases. Emerging approaches to overcome limitations and integrate AI with experimental workflows will be discussed, including multimodal models, physics informed architectures, and closed-loop discovery systems. This review aims to provide insights for researchers aiming to harness AI's transformative potential in accelerating materials discovery for sustainability, healthcare, and energy innovation.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。