Data science-enabled molecular-to-systems engineering for sustainable water treatment

Growing social and economic pressures demand technological innovations that enable the widespread usage of unconventional sources of water. These challenges motivate the emerging fit-for-purpose paradigm, wherein water is provided at the precise quality level of the intended application. Unfortunately, to date, fundamental advances in materials and nanotechnology have been slow to advance this paradigm. Using examples from membrane science and engineering, we highlight the critical need to bridge research at the molecular and nano-scales with development at the device and systems-scales to fully realize sustainable fit-for-purpose water technology. Specifically, we present four opportunities for computing and data science to accelerate convergence of sustainable water research: materials informatics and inverse design, model-based design of experiments, superstructure optimization, and uncertainty quantification. As such, we highlight opportunities to collaboratively revolutionize molecular-to-systems engineering of sustainable water technologies, but emphasize open communication between data scientists and water-focused researchers using a common vocabulary as a significant hurdle.