Laboratory for Atomistic and Molecular Mechanics (LAMM), Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, 77 Massachusetts Ave. 1-290, Cambridge, Massachusetts 02139, USA.
Mater Horiz. 2021 Apr 1;8(4):1153-1172. doi: 10.1039/d0mh01451f. Epub 2021 Jan 7.
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.
人工智能，尤其是机器学习 (ML) 和深度学习 (DL) 算法，正在成为材料和机械工程领域的重要工具，这归因于其预测材料性能、设计全新材料以及发现超越直觉的新机制的能力。随着新型材料结构的复杂性不断提高，优化机械性能的材料设计问题可能涉及到大量难以用传统方法处理的设计空间。为了解决这一挑战，基于机器学习的模型从多个层次的结构、性能和功能相关的大型材料数据集进行训练，为快速探索设计空间提供了新的途径。基于机器学习的材料设计方法的性能取决于数据集的收集或生成，该数据集需要使用材料科学的基础知识（包括化学和物理概念）对其进行适当的预处理，并选择合适的应用机器学习模型。最近，机器学习技术的突破不仅为克服长期存在的力学问题创造了巨大的机会，也为开发前所未有的材料设计策略创造了巨大的机会。在这篇综述中，我们首先简要介绍了最先进的机器学习模型、算法和结构。然后，我们讨论了数据收集、生成和预处理的重要性。总结了基于机器学习的方法在机械性能预测、材料设计和计算方法中的应用，并对这一新兴且令人兴奋的领域的机遇和开放挑战进行了展望。
