Jupyter Notebooks have become increasingly popular in scientific machine learning (SciML) communities for their flexibility in combining code, analysis, and documentation, as well as their ability to facilitate reproducibility and collaboration. However, when it comes to effective results communication, especially for broader or non-technical audiences, Jupyter Notebooks can fall short due to their lack of advanced interactivity and intuitive user interfaces. Dashboards, on the other hand, provides an interactive and visually intuitive solution to present complex scientific results. In recent years, Dash has emerged as a leading tool for creating such dashboards, which not only offers a suite of powerful yet easy-to-program interactivity and visualization features, but also allows for seamless integration into the Jupyter Notebook environment. Therefore, combining Jupyter's analytical capabilities in the backend with Dash's user-friendly interfaces in the frontend could be a way forward for a more effective SciML results communication. In this work, we showcase four distinct applications of Dash that could significantly enhance the communication and accessibility of SciML results: 1. Storytelling: To present the SciML project results, we could build a dashboard organized by sections, which creates narratives to guide users through complex data and insights in a structured, interactive format. 2. Algorithm illustration: To illustrate a specific SciML algorithm, the dashboard could be built to allow users to interactively adjust parameters and visualize changes in real time. 3. Application interface: To serve the trained SciML models, a dashboard could act as the frontend to enable users to input data, visualize predictions, and adjust model inference parameters without needing deep technical expertise. 4. LLM-powered chatbot integration: a chatbot powered by the fast-evolving large language models (e.g., GPT-4) can also be built into the dashboard. This way, we enable context-aware interactions within the dashboard, allowing users to ask questions and receive insights about the SciML models and their results. Live demonstrations for each of the above applications will be given during the presentation.