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Abstract BackgroundComputational approaches in STEM foster creative extrapolations of ideas that extend the bounds of human perception, processing, and sense-making. Inviting teachers to explore computational approaches in STEM presents opportunities to examine shifting relationships to inquiry that support transdisciplinary learning in their classrooms. Similarly, play has long been acknowledged as activity that supports learners in taking risks, exploring the boundaries and configurations of existing structures, and imagining new possibilities. Yet, play is often overlooked as a crucial element of STEM learning, particularly for adolescents and adults. In this paper, we explorecomputational playas an activity that supports teachers’ transdisciplinary STEM learning. We build from an expansive notion of computational activity that involves jointly co-constructing and co-exploring rule-based systems in conversation with materials, collaborators, and communities to work towards jointly defined goals. We situate computation within STEM-rich making as a playful context for engaging in authentic, creative inquiry. Our research asksWhat are the characteristics of play and computation within computational play? And, in what ways does computational play contribute to teachers’ transdisciplinary learning? ResultsTeachers from grades 3–12 participated in a professional learning program that centered playful explorations of materials and tools using computational approaches: making objects based on rules that produce emergent behaviors and iterating on those rules to observe the effects on how the materials behaved. Using a case study and descriptions of the characteristics of computational play, our results show how familiarity of materials and the context of play encouraged teachers to engage in transdisciplinary inquiry, to ask questions about how materials behave, and to renegotiate their own relationships to disciplinary learning as they reflected on their work. ConclusionsWe argue computational play is a space of wonderment where iterative conversations with materials create opportunities for learners to author forms of transdisciplinary learning. Our results show how teachers and students can learn together in computational play, and we conclude this work can contribute to ongoing efforts in the design of professional and transdisciplinary learning environments focused on the intersections of materiality, play, and computation.
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This content will become publicly available on October 1, 2026
Computation and machine learning for materials: Past, present, and future perspectives
AbstractComputational methods and machine learning (ML) are reshaping materials science by accelerating their discovery, design, and optimization. Traditional approaches such as density functional theory and molecular dynamics have been instrumental in studying materials at the atomic level. However, their high computational cost and, in certain cases, limited accuracy can restrict the scope ofin silicoexploration. ML promises to accelerate material property prediction and design. However, in many areas, the volume and fidelity of the data are critical barriers. Active learning can reduce the reliance on large data sets, and simulation has emerged as a critical tool for generating data on the fly. Despite these advances, challenges remain, particularly in data quality, model interpretability, and bridging the gap between computational predictions and experimental validation. Future research should develop automated frameworks capable of designing and testing materials for specific applications, and integrating ML with traditional simulations and experiments can contribute to this goal. Graphic abstract
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- PAR ID:
- 10649485
- Publisher / Repository:
- MRS
- Date Published:
- Journal Name:
- MRS Bulletin
- Volume:
- 50
- Issue:
- 10
- ISSN:
- 0883-7694
- Page Range / eLocation ID:
- 1212 to 1224
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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