Artificial intelligence and machine learning in design of mechanical materials. Issue 4 (7th January 2021)

• Record Type: Journal Article
• Authors: Guo, Kai; Yang, Zhenze; Yu, Chi-Hua; Buehler, Markus J.
• Is Part Of: Materials horizons. Volume 8:Issue 4(2021)
• Page Start: 1153
• Page End: 1172
• Publication Date: 2021-01-07
• Subjects: Materials -- Research -- Periodicals; 543.0284
• DOI: 10.1039/d0mh01451f
• Languages: English
• ISSNs: 2051-6347

Abstract

Abstract : This review revisits the state of the art of research efforts on the design of mechanical materials using machine learning. Abstract : Artificial intelligence, especially machine learning (ML) and deep learning (DL) algorithms, is becoming an important tool in the fields of materials and mechanical engineering, attributed to its power to predict materials properties, design de novo materials and discover new mechanisms beyond intuitions. As the structural complexity of novel materials soars, the material design problem to optimize mechanical behaviors can involve massive design spaces that are intractable for conventional methods. Addressing this challenge, ML models trained from large material datasets that relate structure, properties and function at multiple hierarchical levels have offered new avenues for fast exploration of the design spaces. The performance of a ML-based materials design approach relies on the collection or generation of a large dataset that is properly preprocessed using the domain knowledge of materials science underlying chemical and physical concepts, and a suitable selection of the applied ML model. Recent breakthroughs in ML techniques have created vast opportunities for not only overcoming long-standing mechanics problems but also for developing unprecedented materials design strategies. In this review, we first present a brief introduction of state-of-the-art ML models, algorithms and structures. Then, we discuss the importance of data collection, generation and preprocessing. The applications in mechanical property prediction, materials design and computational methods using ML-based approaches are summarized, followed by perspectives on opportunities and open challenges in this emerging and exciting field.