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This poster presents our first steps to define a roadmap to robust science for high-throughput applications used in scientific discovery. These applications combine multiple components into increasingly complex multi-modal workflows that are often executed in concert on heterogeneous systems. The increasing complexity hinders the ability of scientists to generate robust science (i.e., ensuring performance scalability in space and time; trust in technology, people, and infrastructures; and reproducible or confirmable research). Scientists must withstand and overcome adverse conditions such as heterogeneous and unreliable architectures at all scales (including extreme scale), rigorous testing under uncertainties, unexplainable algorithms in machine learning, and black-box methods. This poster presents findings and recommendations to build a roadmap to overcome these challenges and enable robust science. The data was collected from an international community of scientists during a virtual world cafe in February 2021 

We demonstrate how a chemistry unit on evaporative cooling with an embedded system modeling tool called SageModeler can help students in the critical work of exploring and understanding complex problems. The framework for scaffolding students in ST and CT through modeling can be applied to other disciplines—including climate change, ecosystems, population dynamics, forces and motion, and many other topics—in order to foster student participation in systems thinking and computational thinking through modeling. In doing so, they will build skills to help them solve complex problems of the future.