Artificial intelligence approaches for energetic materials by design: state of the art, challenges, and future directions
Artificial intelligence approaches for energetic materials by design: state of the art, challenges, and future directions
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DOI：
10.1002/prep.202200276
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发表时间：
2022-11
期刊：
影响因子：
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通讯作者：
Joseph B. Choi;Phong C. H. Nguyen;O. Sen;H. Udaykumar;Stephen Seung-Yeob Baek
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文献类型：
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作者：
Joseph B. Choi;Phong C. H. Nguyen;O. Sen;H. Udaykumar;Stephen Seung-Yeob Baek
人工智能（AI）正迅速成为解决各种复杂材料设计问题的使能工具。本文旨在综述人工智能驱动的材料设计及其在含能材料（EM）中的应用的最新进展。通过数值模拟和/或物理实验数据的训练，AI模型可以吸收设计参数空间内的趋势和模式，识别最佳材料设计（微观形态，复合材料中材料的组合等），并指向具有上级/目标属性和性能度量的设计。我们回顾了关于材料设计的三个主要阶段，即微观结构形态的表征学习（即，形状描述符）、结构-性质-性能（S-P-P）关联估计以及优化/设计探索。我们提供了一个透视图，这些方法的潜力，实用性和有效性，实现材料的设计。具体而言，在文献中的方法进行评估，从他们的能力，从一个小/有限数量的数据，计算复杂性，可推广性/可扩展性，其他材料种类和操作条件，模型预测的可解释性，监督/数据注释的负担。最后，我们提出了一些有前途的未来研究方向EM材料的设计，如元学习，主动学习，贝叶斯学习，半/弱监督学习，弥合机器学习研究和EM研究之间的差距。
Artificial intelligence (AI) is rapidly emerging as an enabling tool for solving various complex materials design problems. This paper aims to review recent advances in AI-driven materials-by-design and their applications to energetic materials (EM). Trained with data from numerical simulations and/or physical experiments, AI models can assimilate trends and patterns within the design parameter space, identify optimal material designs (micro-morphologies, combinations of materials in composites, etc.), and point to designs with superior/targeted property and performance metrics. We review approaches focusing on such capabilities with respect to the three main stages of materials-by-design, namely representation learning of microstructure morphology (i.e., shape descriptors), structure-property-performance (S-P-P) linkage estimation, and optimization/design exploration. We provide a perspective view of these methods in terms of their potential, practicality, and efficacy towards the realization of materials-by-design. Specifically, methods in the literature are evaluated in terms of their capacity to learn from a small/limited number of data, computational complexity, generalizability/scalability to other material species and operating conditions, interpretability of the model predictions, and the burden of supervision/data annotation. Finally, we suggest a few promising future research directions for EM materials-by-design, such as meta-learning, active learning, Bayesian learning, and semi-/weakly-supervised learning, to bridge the gap between machine learning research and EM research.