Artificial Intelligence in Material Engineering: A Review on Applications of Artificial Intelligence in Material Engineering

Abstract

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.

Enhancing Nanocrystal Synthesis: A Comparative Study of Online Artificial Intelligence Optimization and Offline High-Throughput Experimentation in Chemical Material Discovery

“…3,4 Meanwhile, AI in material science has evolved from fundamental algorithms to sophisticated models, enhancing its predictive capabilities and efficiency in property evaluation. 5 This evolution is marked by AI's synergy with high-performance computing and robotics, greatly accelerating discovery cycles. 6,7 AI's adoption in this domain is fueled by the demand for quicker, more precise experimental processes and the management of chemical research's inherent complexity.…”

“…AI has revolutionized chemical material experimentation, transitioning from conventional, manual methods to automated, data-centric techniques. , Meanwhile, AI in material science has evolved from fundamental algorithms to sophisticated models, enhancing its predictive capabilities and efficiency in property evaluation . This evolution is marked by AI’s synergy with high-performance computing and robotics, greatly accelerating discovery cycles. , AI’s adoption in this domain is fueled by the demand for quicker, more precise experimental processes and the management of chemical research’s inherent complexity. , A notable instance is the development of chemically amplified photoresists, where AI, combined with other computational methods, has significantly improved the efficiency and accuracy. − AI’s recent advancements have also digitalized key tasks in chemical synthesis, such as predicting reactions, analyzing retrosynthetic pathways, and developing new experimental protocols. − This shift toward digitalization has spurred a wave of automation in chemical synthesis utilizing advanced hardware and robotics to undertake tasks previously performed manually .…”

https://2DMat.ChemDX.org: Experimental data platform for 2D materials from synthesis to physical properties

“…In recent years, integrating data science and artificial intelligence (AI) techniques into scientific research has triggered a transformative shift, particularly in materials exploration and optimization. 1–11 This shift has significantly emphasized the values of “data” generated from experimental and computational processes, leading to the emergence of materials data platforms as indispensable tools in data-driven research. 12–17 These platforms facilitate convenient data collection, integration, utilization, and sharing.…”