mlsanityData to 14 Oct 2026

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

arXiv 2211.08179 ↗First posted 15 Nov 2022cond-mat.mtrl-sci cs.LGcited by 6

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Abstract (arXiv)

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.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 42 of its 173 references that are in the map. First posted on arXiv 15 Nov 2022.

Builds on

42 of 173 references.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 2; 2025: 2; 2026: 2.

6 papers.

  1. 2024Constrained Synthesis with Projected Diffusion Modelscited by 89
  2. 2024Multi-Task Multi-Fidelity Learning of Properties for Energetic Materialscited by 2
  3. 2025A physics-aware deep learning model for shear band formation around collapsing pores in shocked reactive materialscited by 1
  4. 2025Multi-resolution Physics-Aware Recurrent Convolutional Neural Network for Complex Flowscited by 1
  5. 2026Generative Chemical Language Models for Energetic Materials Discoverycited by 0
  6. 2026Domain-Gated Latent Diffusion: Generative Inverse Design of HMX-Class Energetic Materials with First-Principles Validationcited by 0