The role of artificial intelligence (AI) in material science and engineering (MSE) is becoming increasingly important as AI technology advances. The development of high-performance computing has made it possible to test deep learning (DL) models with significant parameters, providing an opportunity to overcome the limitation of traditional computational methods, such as density functional theory (DFT), in property prediction. Machine learning (ML)-based methods are faster and more accurate than DFT-based methods. Furthermore, the generative adversarial networks (GANs) have facilitated the generation of chemical compositions of inorganic materials without using crystal structure information. These developments have significantly impacted material engineering (ME) and research. Some of the latest developments in AI in ME herein are reviewed. First, the development of AI in the critical areas of ME, such as in material processing, the study of structure and material property, and measuring the performance of materials in various aspects, is discussed. Then, the significant methods of AI and their uses in MSE, such as graph neural network, generative models, transfer of learning, etc. are discussed. The use of AI to analyze the results from existing analytical instruments is also discussed. Finally, AI's advantages, disadvantages, and future in ME are discussed.

翻译：随着人工智能技术的进步，人工智能在材料科学与工程领域的作用日益重要。高性能计算的发展使得测试具有显著参数的深度学习模型成为可能，为克服传统计算方法（如密度泛函理论）在性质预测中的局限性提供了机会。基于机器学习的方法比基于密度泛函理论的方法更快、更精确。此外，生成对抗网络促进了无需晶体结构信息的无机材料化学成分的生成。这些发展对材料工程和研究产生了重大影响。本文综述了人工智能在材料工程中的一些最新进展。首先，讨论了人工智能在材料工程关键领域的发展，例如材料加工、结构与材料性质研究以及材料各方面性能的测量。随后，探讨了人工智能的主要方法及其在材料科学与工程中的应用，如图神经网络、生成模型、迁移学习等。还讨论了利用人工智能分析现有分析仪器结果的方法。最后，讨论了人工智能在材料工程中的优势、劣势及未来前景。