Artificial Intelligence to Power the Future of Materials Science and Engineering

TLDR

AI has attracted attention for its potential to automate and accelerate productivity, and recent advances in data, computing, and deep learning have enabled its application to materials research, offering a faster alternative to inefficient trial‑and‑error methods that rely on hard‑coded formulas. The paper reviews AI applications in material innovation, covering design, performance prediction, and synthesis, and discusses future development directions from algorithm and infrastructure perspectives. The review examines AI methods applied to material design, performance prediction, and synthesis. The review highlights how AI techniques are realized and their advantages over conventional methods in these applications.

Abstract

Artificial intelligence (AI) has received widespread attention over the last few decades due to its potential to increase automation and accelerate productivity. In recent years, a large number of training data, improved computing power, and advanced deep learning algorithms are conducive to the wide application of AI, including material research. The traditional trial‐and‐error method is inefficient and time‐consuming to study materials. Therefore, AI, especially machine learning, can accelerate the process by learning rules from datasets and building models to predict. This is completely different from computational chemistry where a computer is only a calculator, using hard‐coded formulas provided by human experts. Herein, the application of AI in material innovation is reviewed, including material design, performance prediction, and synthesis. The realization details of AI techniques and advantages over conventional methods are emphasized in these applications. Finally, the future development direction of AI is expounded from both algorithm and infrastructure aspects.