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Additive manufacturing (AM) enables the fabrication of complex, highly customized geometries. However, the design and fabrication of structures with advanced functionalities, such as multistability and fail-safe mechanism, remain challenging due to the significant time and costs required for high-fidelity simulations and iterative prototyping. In this study, we investigate the application of Bayesian Optimization (BO), an advanced machine learning framework, to accelerate the discovery of optimal AM compatible designs with such advanced properties. BO uses a probabilistic surrogate to strategically balances the exploration of design space with few test designs and the exploitation of design space near current best performing designs, thereby reducing the number of design simulations needed. While existing studies have demonstrated the potential of BO in AM, most have focused on static or simple designs. Here, we target multistable structures that can reconfigure among multiple stable states in response to external conditions. Since mechanical performance (e.g., strength) is configuration-dependent, our goal is to identify high performing designs while ensuring that strength in all stable configurations exceeds a prescribed threshold for structural robustness. 

Abstract Computational thinking is crucial for STEM researchers and practitioners, as it involves more than just developing skills—it is a way of thinking that enables effective problem-solving. STEM disciplines approach different problems and as such employ computational thinking uniquely, so students cannot rely solely on computer science to develop computational thinking. Less attention has been given to social aspects of computation, such as collaborating and communicating with and about computation even though social aspects are essential to problem solving. We utilized computational literacy as an alternative framework that explicitly includes social elements as a primary pillar. We conducted 15 interviews with STEM researchers to identify and organize the social aspects that play a role in their research. We organized goals by motivation (persuasion and productivity) and representation (visual and non-visual) to contextualize the use of communication in computation. We found that researchers use computation to explain research results, navigate decision making, establish rigor, ensure reproducibility, facilitate lab stability, and promote research efficiency. We used Activity Theory to describe the tools, norms, and communities associated with these goals to offer a more detailed framework for the social pillar of computational literacy within the context of science and engineering. Examples from each discipline within STEM are described. This social computational literacy framework can act as a guide for STEM educators and practitioners alike to use and teach social aspects of computation.