Artificial intelligence and machine learning in design of mechanical materials.
By Kai Guo, Zhenze Yang, Chi-Hua Yu, Markus J. Buehler. Materials Horizons, 2021
471 citations recorded by the source corpus.
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.
Subfields: Materials Chemistry, Mechanical Engineering
View Paper: https://www.semanticscholar.org/paper/3b750e38452b3a6a555e3534178984973e69ddf9
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In Materials Chemistry
In Mechanical Engineering
Citations in the corpus
- Physics-informed neural networks with hard constraints for inverse design (2021, methodology): “Recently, deep learning in the form of deep neural networks has been used in many areas of inverse design, including nanophotonics [52, 17], mechanical materials [15], aerodynamics [49], and electromagnetism [47].”
- The shape – morphing performance of magnetoactive soft materials (2021, background): “Artificial intelligence (AI) such as machine learning and data-driven modelling is extensively developed to optimise compositions of composite materials [262-264].”