One universal translator for all dialects of sodium. Liquid sodium in a reactor behaves differently depending on where it is: near the pipe wall it is dense and fast, near the boiling surface it is rarefied and slow. Previously, each such state required its own set of calculations, as if you had to hire a new translator for every phrase.

Now a single neural network potential handles all the 'dialects' at once, from room temperature, where sodium is a solid metal, to the critical point, where liquid and vapor become indistinguishable. The accuracy is close to experimental: the density of solid sodium differs from the measured value by only 0.06%.

To keep the translator from making mistakes in 'tone', that is, in energy, they added a separate model for electronic entropy. This allowed them to recover the internal energy and calculate caloric properties. Without such a model, the potential would only know forces, not heat.

The universal translator for sodium is ready. It ties together the equation of state, phase boundaries, acoustics, and transport into one consistent picture. For fast reactors, this means the coolant's properties can be predicted all the way up to accident scenarios with overheating, without waiting for dangerous experiments.