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Directly generating material structures with optimal properties is a long-standing goal in material design. Traditional generative models often struggle to efficiently explore the global chemical space, limiting their utility to localized space. Here, we present a framework named Material Generation with Efficient Global Chemical Space Search (MAGECS) that addresses this challenge by integrating the bird swarm algorithm and supervised graph neural networks, enabling effective navigation of generative models in the immense chemical space towards materials with target properties.

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Navigating the challenges of ST-segment elevation myocardial infarction in women: A closer look.

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Objective: To understand parents' experiences of ethical challenges in the care of children with chronic and serious medical conditions and what resources they access for support.

Study Design: We recruited English-speaking parents of children with complex and serious medical conditions via family advocacy groups to complete an electronic survey from October 2022 through February 2023. We queried respondents' experiences with specific ethical challenges in the care of their child, whether their concerns persisted, and what resources they accessed.

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