Artificial Intelligence to Power the Future of Materials Science and Engineering

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.

Gas sensing based on organic composite materials: Review of sensor types, progresses and challenges

“…Recently, artificial intelligence has attracted the attention of chemical engineering research. For example, it can help laboratory researchers find the optimal parameters to achieve a useful result and facilitate the process of manufacturing materials [116]. In addition, artificial intelligence can be used in the decision-making part of the detection system to identify the detected gas using non-linear / linear and/or unsupervised / supervised algorithms [117].…”

Artificial Intelligence in Materials Science

“…For sure, AI will not completely replace human at the work of material research but will serve as a powerful tool to accelerate the progress of materials discovery. Material researchers will need to learn to master AI tools to decrease the trial error times, solve more difficult material problems [11].…”

Application of machine learning and genetic optimization algorithms for modeling and optimizing soybean yield using its component traits

“…Due to the essential need for advanced skills in computational and mathematical analyses, exploiting large datasets in many public breeding programs is still a bottleneck. Machine Learning (ML) algorithms, as one of the reliable and efficient computational approaches, were successfully implemented in different fields of study, such as traffic crash frequency modeling [22,23], environmental science [24], engineering [25], and medicine [26]. Previously, Zeng, Huang (22) developed neural network, as one of the ML algorithms for exploring the non-linear relationship between risk factors and crash frequency.…”