2026-03-102026-03-10https://hdl.handle.net/11420/61977The characteristic behavior of water-driven materials results to a large extent from complex and as yet often poorly understood interactions across different scales. To unravel the fundamental physics and chemistry of water-driven materials, (1) we will develop novel multiscale modeling methods bridging from the quantum to the device level; (2) machine learning (ML) will help to bridge between the scales and leverage the unique imaging data collected in the CA Imaging. Including physical knowledge into ML architectures will help accelerate the design of water-driven materials by identifying, for example, process-structure-property relationships in a fast and automated manner (physics-informed ML). To achieve these ambitious goals, we will rely on extensive (3) research software engineering, providing a whole ecosystem of cutting-edge open-source research software. Overall, we will create a unique multiscale and multi-purpose open-source software ecosystem for computer simulation and ML to understand, predict and optimize Blue Materials from the quantum level to the device level, laying the foundations to leverage cutting-edge imaging and AI to go from “nature-inspired materials” to materials and functionalities “beyond nature”