A programação de computadores é um conhecimento complexo porque para ser aprendido demanda a operacionalização de várias habilidades. No caso de estudantes surdos, o desafio é ainda maior pois a aprendizagem é dificultada pelas limitações no domínio da segunda língua que impactam diretamente nas habilidades de interpretação textual, na compreensão do problema, na sequenciação lógica de algoritmos, na escrita e na depuração de programas, que são habilidades fundamentais no processo de programação. Considerando os desafios do ensino de programação para surdos, este trabalho recomenda ações e tecnologias assistivas, propõe a extensão de um curso de programação a distância a partir de vídeos assistivos produzidos dinamicamente no ensino presencial e apresenta relatos de experiências de produção de vídeos assistivos para um curso de programação a distância.

The development of spintronic devices demands the existence of materials with some kind of spin splitting (SS). In this Data Descriptor, we build a database of ab initio calculated SS in 2D materials. More than that, we propose a workflow for materials design integrating an inverse design approach and a Bayesian inference optimization. We use the prediction of SS prototypes for spintronic applications as an illustrative example of the proposed workflow. The prediction process starts with the establishment of the design principles (the physical mechanism behind the target properties), that are used as filters for materials screening, and followed by density functional theory (DFT) calculations. Applying this process to the C2DB database, we identify and classify 358 2D materials according to SS type at the valence and/or conduction bands. The Bayesian optimization captures trends that are used for the rationalized design of 2D materials with the ideal conditions of band gap and SS for potential spintronics applications. Our workflow can be applied to any other material property.

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The development of spintronic devices demands the existence of materials with some kind of spin splitting (SS). In this Data Descriptor, we build a database of ab initio calculated SS in 2D materials. More than that, we propose a workflow for materials design integrating an inverse design approach and a Bayesian inference optimization. We use the prediction of SS prototypes for spintronic applications as an illustrative example of the proposed workflow. The prediction process starts with the establishment of the design principles (the physical mechanism behind the target properties), that are used as filters for materials screening, and followed by density functional theory (DFT) calculations. Applying this process to the C2DB database, we identify and classify 358 2D materials according to SS type at the valence and/or conduction bands. The Bayesian optimization captures trends that are used for the rationalized design of 2D materials with the ideal conditions of band gap and SS for potential spintronics applications. Our workflow can be applied to any other material property.

No abstract