Designing materials for 21st Century challenges like clean energy and advanced technology requires understanding the behavior of electrons. DeepMind has proposed a data-driven neural network called DM21 to accurately model electron behavior for chemistry simulations. This technology addresses problems with traditional functionals, such as delocalization error and spin symmetry breaking, by using a neural network to represent the functional and tailoring the training dataset to capture fractional electron behavior. The use of deep learning in quantum simulations shows promise for understanding complex molecular interactions in the future of technology. Check out the full research paper here.