Machine Learning for Inverse Design and Optimization of Metamaterials

In the domain of metamaterials, the push toward automated design has been accelerated by advances in generative machine learning. The advent of deep learning models, such as Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs), have significantly widened the design space, for creating complex architectures with programmable properties. Despite this progress, inverse design remains a fundamental challenge, as it requires deriving internal micro- or meso-scale structures from targeted macroscopic responses. The challenge is particularly pronounced for highly intricate or irregular systems, such as kirigami-based metamaterials. Recent findings have highlighted the limitations of existing generative models in accurately capturing the nuanced structure-property relationship characteristic of these advanced materials, underscoring the need for innovative data-driven frameworks.Recent studies show that deep generative models and Bayesian optimization techniques have begun to reveal promising results in handling the inverse design problem for metamaterials. However, these approaches still struggle to generalize to broader classes of complex architectures and often face obstacles related to training data availability, model interpretability, and efficient exploration of high-dimensional design spaces. As research increasingly integrates machine learning innovation with materials science, it becomes clear that further methodological refinements and hybrid modelling strategies are needed to bridge critical capability gaps. This ongoing research highlights the importance of crafting robust, adaptable design frameworks capable of managing the challenges posed by irregularly architected metamaterials. This Research Topic aims to advance the development of next-generation generative machine learning models to improve the accuracy and efficiency of inverse design and optimization processes for metamaterials. Key goals include addressing the unresolved challenges of structure-property prediction, refining existing generative architectures, and enhancing the integration of interpretability, data efficiency, and optimization techniques.The scope of this Research Topic encompasses advances in both methodological innovation and practical application of generative machine learning for metamaterial design within well-defined boundaries. Submissions should explore innovations that distinctly address inverse design challenges by proposing optimization strategies for advanced metamaterial systems. To gather further insights in these areas, we welcome articles addressing, but not limited to, the following themes:- Novel generative models for inverse metamaterial design- Structure-property relationship discovery in architected materials- Data-efficient learning and optimization strategies for complex design spaces- Improvements in model interpretability and explainability- Hybrid frameworks combining physics-based models with data-driven approaches- Applications of generative design in programmable or reconfigurable metamaterials- Benchmarks, datasets, and standardization in computational metamaterial designContributions that interrogate both theoretical advances and practical implementations are encouraged, with a particular emphasis on interdisciplinary perspectives and data-centric strategies. Topic editors: Shaoping Xiao, Tae Yeon Kim, Qingcheng Yang, Stéphane Pierre Alain BORDAS Source: https://www.frontiersin.org/research-topics/77232/machine-learning-for-inverse-design-and-optimization-of-metamaterials