Artificial intelligence approaches for materials-by-design of energetic materials: state-of-the-art, challenges, and future directions

• URL: http://arxiv.org/abs/2211.08179v2
• Date: Mon, 27 Mar 2023 03:29:48 GMT
• Title: Artificial intelligence approaches for materials-by-design of energetic materials: state-of-the-art, challenges, and future directions
• Authors: Joseph B. Choi, Phong C. H. Nguyen, Oishik Sen, H. S. Udaykumar, Stephen Baek
• Abstract summary: We review advances in AI-driven materials-by-design and their applications to energetic materials. We evaluate methods in the literature in terms of their capacity to learn from a small/limited number of data. 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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