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1. t-METASET: Task-Aware Acquisition of Metamaterial Datasets Through Diversity-Based Active Learning

   https://doi.org/10.1115/1.4055925  

   Lee, Doksoo; Chan, Yu-Chin; Chen, Wei; Wang, Liwei; van Beek, Anton; Chen, Wei (March 2023, Journal of Mechanical Design)

   Abstract Inspired by the recent achievements of machine learning in diverse domains, data-driven metamaterials design has emerged as a compelling paradigm that can unlock the potential of multiscale architectures. The model-centric research trend, however, lacks principled frameworks dedicated to data acquisition, whose quality propagates into the downstream tasks. Often built by naive space-filling design in shape descriptor space, metamaterial datasets suffer from property distributions that are either highly imbalanced or at odds with design tasks of interest. To this end, we present t-METASET: an active learning-based data acquisition framework aiming to guide both diverse and task-aware data generation. Distinctly, we seek a solution to a commonplace yet frequently overlooked scenario at early stages of data-driven design of metamaterials: when a massive (∼O(104)) shape-only library has been prepared with no properties evaluated. The key idea is to harness a data-driven shape descriptor learned from generative models, fit a sparse regressor as a start-up agent, and leverage metrics related to diversity to drive data acquisition to areas that help designers fulfill design goals. We validate the proposed framework in three deployment cases, which encompass general use, task-specific use, and tailorable use. Two large-scale mechanical metamaterial datasets are used to demonstrate the efficacy. Applicable to general image-based design representations, t-METASET could boost future advancements in data-driven design. 

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2. Symmetry-Induced Mechanical Metamaterials Design Framework and Dataset Generation

   https://doi.org/10.1115/DETC2024-141866  

   Abu-Mualla, Mohammad; Huang, Jida (August 2024, American Society of Mechanical Engineers)

   Abstract The surge in machine learning research and recent advancements in 3D printing technologies have significantly enriched materials science and engineering, particularly in the domain of mechanical metamaterials, which commonly consist of periodic truss materials. Despite the extensive exploration of their tailorable properties, truss-based metamaterial design has predominantly adhered to cubic and orthotropic unit-cells, a limitation arising from the conventional design method, where the type of symmetry related to the designed truss-based material is determined after the design process is done. To overcome this issue, this work introduces a groundbreaking 3D truss material designing framework that departs from this constraint by employing six distinctive material symmetries (cubic, hexagonal, tetragonal, orthotropic, trigonal, and monoclinic) within the design process. This innovative approach represents a versatile paradigm shift compared to previous design approaches. Furthermore, we are able to integrate anisotropy into the design framework, thus enhancing the property space exploration capability of the proposed design framework. Probing materials property space using our design framework demonstrates its capacity to achieve a diverse range of mechanical properties, surpassing even the most extensive datasets available in the literature. The proposed method facilitates the generation of a comprehensive truss dataset, which can be represented in a trainable continuous format suitable for machine learning and data-driven approaches. This advancement paves the way for the development of robust inverse design tools for truss materials, marking a significant contribution to the mechanical metamaterial community. 

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3. A Dataset Generation Framework for Symmetry-Induced Mechanical Metamaterials

   https://doi.org/10.1115/1.4066169  

   Abu-Mualla, Mohammad; Huang, Jida (April 2025, Journal of Mechanical Design)

   Abstract The surge in machine learning research and recent advancements in 3D printing technologies have significantly enriched materials science and engineering, particularly in the domain of mechanical metamaterials, which commonly consist of periodic truss materials. Despite the extensive exploration of their tailorable properties, truss-based metamaterial design has predominantly adhered to cubic and orthotropic unit cells, a limitation arising from the conventional design method, where the type of symmetry related to the designed truss-based material is determined after the design process is done. To overcome this issue, this work introduces a groundbreaking 3D truss material designing framework that departs from this constraint by employing six distinctive material symmetries (cubic, hexagonal, tetragonal, orthotropic, trigonal, and monoclinic) within the design process. This innovative approach represents a versatile paradigm shift compared to previous design approaches. Furthermore, we are able to integrate anisotropy into the design framework, thus enhancing the property space exploration capability of the proposed design framework. Probing the property space of unit cells using our design framework demonstrates its capacity to achieve a diverse range of mechanical properties. The analysis of the generated samples shows that they can surpass the most extensive datasets available in the literature in regions where directional elastic properties are not linked by structural symmetry. The proposed method facilitates the generation of a truss dataset, which can be represented in a trainable format suitable for machine learning and data-driven approaches. This advancement paves the way for the development of robust inverse design tools for truss materials, marking a significant contribution to the mechanical metamaterial community. 

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4. Towards overcoming data scarcity in materials science: unifying models and datasets with a mixture of experts framework

   https://doi.org/10.1038/s41524-022-00929-x  

   Chang, Rees; Wang, Yu-Xiong; Ertekin, Elif (November 2022, npj Computational Materials)

   Abstract While machine learning has emerged in recent years as a useful tool for the rapid prediction of materials properties, generating sufficient data to reliably train models without overfitting is often impractical. Towards overcoming this limitation, we present a general framework for leveraging complementary information across different models and datasets for accurate prediction of data-scarce materials properties. Our approach, based on a machine learning paradigm called mixture of experts, outperforms pairwise transfer learning on 14 of 19 materials property regression tasks, performing comparably on four of the remaining five. The approach is interpretable, model-agnostic, and scalable to combining an arbitrary number of pre-trained models and datasets to any downstream property prediction task. We anticipate the performance of our framework will further improve as better model architectures, new pre-training tasks, and larger materials datasets are developed by the community. 

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5. Expediting human motor learning in high-dimensional de-novo tasks via online curriculum design

   Kamboj, Ankur; Ranganathan, Rajiv; Tan, Xiaobo; Srivastava, Vaibhav (July 2025, IEEE)

   While recent advancements in motor learning have emphasized the critical role of systematic task scheduling in enhancing task learning, the heuristic design of task schedules remains predominant. Random task scheduling can lead to sub-optimal motor learning, whereas performance-based scheduling might not be adequate for complex motor skill acquisition. This paper addresses these challenges by proposing a model-based approach for online skill estimation and individualized task scheduling in de-novo (novel) motor learning tasks. We introduce a framework utilizing a personalized human motor learning model and particle filter for skill state estimation, coupled with a stochastic nonlinear model predictive control (SNMPC) strategy to optimize curriculum design for a high-dimensional motor task. Simulation results show the effectiveness of our framework in estimating the latent skill state, and the efficacy of the framework in accelerating skill learning. Furthermore, a human subject study shows that the group with the SNMPC-based curriculum design exhibited expedited skill learning and improved task performance. Our contributions offer a pathway towards expedited motor learning across various novel tasks, with implications for enhancing rehabilitation and skill acquisition processes. 

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