The production of clean water is one of the major scientific, technological, and social challenges of the 21st century. Among the available alternatives, membrane-based desalination has emerged as a promising strategy to convert saline water into potable water, provided that more efficient, selective, and sustainable materials can be developed. In this talk, I will discuss how computational physics can contribute to this challenge by connecting atomic-scale phenomena to the development of membranes and, ultimately, to the operation of desalination plants. Based on studies involving water confined in nanopores, two-dimensional materials, and water/surface interfaces, I will present how atomistic simulations can be used to investigate the mechanisms that control water flux and ion rejection. Factors such as pore size and geometry, polarity, chemical functionalization, water/surface interaction energy, and the molecular organization of confined water will be discussed. I will also address a recent research direction focused on the development of force fields for water based on information obtained from quantum computing, aiming to improve the description of molecular interactions in confined environments. Finally, I will show how machine learning models can be integrated with multiscale simulations to accelerate the screening of promising nanopores and membranes for desalination. This talk aims to present an integrated perspective that goes from the molecule to the membrane, and from the membrane to the plant, showing how quantum computing, ab initio simulations, molecular dynamics, machine learning, and experiments can act in a complementary way in the development of technologies for clean water production.