机械材料设计中的人工智能和机器学习,Materials Horizons

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机械材料设计中的人工智能和机器学习
Materials Horizons ( IF 12.2 ) Pub Date : 2020-12-17 , DOI: 10.1039/d0mh01451f
Kai Guo ₁ , Zhenze Yang , Chi-Hua Yu , Markus J Buehler

Affiliation

人工智能，尤其是机器学习 (ML) 和深度学习 (DL) 算法，正成为材料和机械工程领域的重要工具，这归功于其预测材料特性、从头设计的能力材料并发现超越直觉的新机制。随着新型材料的结构复杂性飙升，优化机械性能的材料设计问题可能涉及传统方法难以处理的大量设计空间。为应对这一挑战，从涉及多个层次结构、属性和功能的大型材料数据集训练的 ML 模型为快速探索设计空间提供了新途径。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. ML 技术的最新突破为克服长期存在的力学问题以及开发前所未有的材料设计策略创造了巨大的机会。在这篇评论中，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，我们讨论数据收集、生成和预处理的重要性。总结了使用基于 ML 的方法在机械性能预测、材料设计和计算方法中的应用，然后展望了这一新兴和令人兴奋的领域中的机遇和开放挑战。我们讨论了数据收集、生成和预处理的重要性。总结了使用基于 ML 的方法在机械性能预测、材料设计和计算方法中的应用，然后展望了这一新兴和令人兴奋的领域中的机遇和开放挑战。我们讨论了数据收集、生成和预处理的重要性。总结了使用基于 ML 的方法在机械性能预测、材料设计和计算方法中的应用，然后展望了这一新兴和令人兴奋的领域中的机遇和开放挑战。

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Artificial intelligence and machine learning in design of mechanical materials

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.

更新日期：2021-01-07

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