Artificial Intelligence in Materials Modeling and Design
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ORIGINAL PAPER
Artificial Intelligence in Materials Modeling and Design J. S. Huang1 · J. X. Liew2 · A. S. Ademiloye3 · K. M. Liew1 Received: 12 March 2020 / Accepted: 30 September 2020 © CIMNE, Barcelona, Spain 2020
Abstract In recent decades, the use of artificial intelligence (AI) techniques in the field of materials modeling has received significant attention owing to their excellent ability to analyze a vast amount of data and reveal correlations between several complex interrelated phenomena. In this review paper, we summarize recent advances in the applications of AI techniques for numerical modeling of different types of materials. AI techniques such as machine learning and deep learning show great advantages and potential for predicting important mechanical properties of materials and reveal how changes in certain principal parameters affect the overall behavior of engineering materials. Furthermore, in this review, we show that the application of AI techniques can significantly help to improve the design and optimize the properties of future advanced engineering materials. Finally, a perspective on the challenges and prospects of the applications of AI techniques for material modeling is presented.
1 Introduction Computer, as an indispensable tool in modern lives, has evolved in a very stunning pace and it is gradually taking the place of humans in many fields. The ever-increasing computing power and storage capacity have made the use of computers for many complicated tasks and systems very attractive. The use of computers with high computing power and storage capacity can help humans to handle large and messy data sets with a good level of precision. Recent advances in the field of computer science and technology have equipped modern computers with the abilities and skills of “self-teaching” and “self-learning” like humans [1–3]. One of the most significant and current discussions in the field of computer science is artificial intelligence (AI). Many of the well-known AI techniques such as cognitive J. S. Huang and J. X. Liew these authors contributed equally to this work. * K. M. Liew [email protected] 1
Department of Architecture and Civil Engineering, City University of Hong Kong, Kowloon, Hong Kong SAR, China
2
Faculty of Applied Science, University of British Columbia, Kelowna, BC V1V 1V7, Canada
3
Zienkiewicz Centre for Computational Engineering, College of Engineering, Swansea University, Bay Campus, Swansea SA1 8EN, United Kingdom
intelligence (CI), machine learning (ML) and deep learning (DL) are fast becoming an essential component for a wide range of modern technologies [4, 5]. For example, AI techniques are widely used in different fields such as genomics [6, 7], drug discovery [8, 9], automation [10] and financial markets analysis [11]. As one of the most famous branches of AI, ML is the science of getting computers to act like humans without being explicitly programmed [12]. It is usually employed to obtained hidden patterns in complicated systems through a training p
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