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1. First impressions matter: An analysis of prospective teachers' noticing of curriculum materials

   Block, Scott; Quaisley, Kelsey; Males, Lorraine M (October 2023, Proceedings of the forty-fifth annual meeting of the North American Chapter of the International Group for the Psychology of Mathematics Education)

   Lamberg, T; Moss, D (Ed.)

   Given the ubiquity of curriculum materials and complexity of their usage, it is imperative that teacher education programs prepare prospective teachers (PSTs) to use curriculum materials. In this paper, we focus on what PSTs notice when they are interacting with curriculum materials, and how their initial impressions of curriculum materials influence their later understandings of curriculum materials. We found that PSTs’ 20-second impressions may be indicative of their longer impressions of curriculum materials, which can include their preferences, values, beliefs, and approaches to using curriculum materials. We suggest that teacher educators expose PSTs to a variety of curriculum materials to better support PSTs in planning and enacting lessons. 

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2. MaterialsAtlas.org: a materials informatics web app platform for materials discovery and survey of state-of-the-art

   https://doi.org/10.1038/s41524-022-00750-6  

   Hu, Jianjun; Stefanov, Stanislav; Song, Yuqi; Omee, Sadman Sadeed; Louis, Steph-Yves; Siriwardane, Edirisuriya M. D.; Zhao, Yong; Wei, Lai (April 2022, npj Computational Materials)

   Abstract The availability and easy access of large-scale experimental and computational materials data have enabled the emergence of accelerated development of algorithms and models for materials property prediction, structure prediction, and generative design of materials. However, the lack of user-friendly materials informatics web servers has severely constrained the wide adoption of such tools in the daily practice of materials screening, tinkering, and design space exploration by materials scientists. Herein we first survey current materials informatics web apps and then propose and develop MaterialsAtlas.org, a web-based materials informatics toolbox for materials discovery, which includes a variety of routinely needed tools for exploratory materials discovery, including material’s composition and structure validity check (e.g. charge neutrality, electronegativity balance, dynamic stability, Pauling rules), materials property prediction (e.g. band gap, elastic moduli, hardness, and thermal conductivity), search for hypothetical materials, and utility tools. These user-friendly tools can be freely accessed athttp://www.materialsatlas.org. We argue that such materials informatics apps should be widely developed by the community to speed up materials discovery processes. 

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3. A Review on the Strengthening of Nanostructured Materials

   https://doi.org/10.14741/ijcet/v.8.2.7  

   Tian, Liang; Li, Lin (January 2018, International Journal of Current Engineering and Technology)

   Nanostructured materials, whose characteristic microstructure size is under 100 nm, can be either single-phasenanocrystalline materials or multi-phase nanocomposite materials. Nanocrystalline materials can also be treated asnanocomposites with grain interior as matrix and grain boundary as secondary phase. The strengthening models ofnanostructured materials resemble those strengthening models of conventional composite structures, but havesubstantial deviations from conventional strengthening mechanisms due to their distinctive nanoscale structure andthe complex hierarchy of their nanoscale microstructure. This paper reviewed the current progress in developmentsof strengthening models for nanostructured materials with emphasis on single-phase nanocrystalline and multiphasenanocomposite materials, which would help guide the design of new nanostructured materials and othersimilar nanoscale composite structures. Furthermore, practical large scale industrial applications of high strengthnanostructured materials require these materials to possess decent formability, ductility or other functionalproperties to satisfy both structural and multifunctional applications. Therefore, the latest developments of novelnanostructured materials are discussed to highlight their potential of overcoming the strength ductility trade-off andstrength-conductivity trade-off by various approaches. Their complex and distinctive nanoscale microstructuresuggests the potential challenges and opportunities in developing new strengthening models for designing futureadvanced nanostructured materials with unprecedented properties. 

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4. Data-Driven Studies of Li-Ion-Battery Materials

   https://doi.org/10.3390/cryst9010054  

   Kauwe, Steven; Rhone, Trevor; Sparks, Taylor (January 2019, Crystals)

   Batteries are a critical component of modern society. The growing demand for new battery materials—coupled with a historically long materials development time—highlights the need for advances in battery materials development. Understanding battery systems has been frustratingly slow for the materials science community. In particular, the discovery of more abundant battery materials has been difficult. In this paper, we describe how machine learning tools can be exploited to predict the properties of battery materials. In particular, we report the challenges associated with a data-driven investigation of battery systems. Using a dataset of cathode materials and various statistical models, we predicted the specific discharge capacity at 25 cycles. We discuss the present limitations of this approach and propose a paradigm shift in the materials research process that would better allow data-driven approaches to excel in aiding the discovery of battery materials. 

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5. High‐Throughput Discovery of Novel Cubic Crystal Materials Using Deep Generative Neural Networks

   https://doi.org/10.1002/advs.202100566  

   Zhao, Yong; Al‐Fahdi, Mohammed; Hu, Ming; Siriwardane, Edirisuriya_M_D; Song, Yuqi; Nasiri, Alireza; Hu, Jianjun (August 2021, Advanced Science)

   Abstract High‐throughput screening has become one of the major strategies for the discovery of novel functional materials. However, its effectiveness is severely limited by the lack of sufficient and diverse materials in current materials repositories such as the open quantum materials database (OQMD). Recent progress in deep learning have enabled generative strategies that learn implicit chemical rules for creating hypothetical materials with new compositions and structures. However, current materials generative models have difficulty in generating structurally diverse, chemically valid, and stable materials. Here we propose CubicGAN, a generative adversarial network (GAN) based deep neural network model for large scale generative design of novel cubic materials. When trained on 375 749 ternary materials from the OQMD database, the authors show that the model is able to not only rediscover most of the currently known cubic materials but also generate hypothetical materials of new structure prototypes. A total of 506 such materials have been verified by phonon dispersion calculation. Considering the importance of cubic materials in wide applications such as solar panels, the GAN model provides a promising approach to significantly expand existing materials repositories, enabling the discovery of new functional materials via screening. The new crystal structures discovered are freely accessible atwww.carolinamatdb.org. 

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