Description
Finite temperature atomistic simulations require high accuracy dynamical models with much better performance than direct ab initio calculations. Two of those are high-order polynomial expansions and neural network force fields. We discuss high-performance implementations of both using JAX and point out the intrinsic limitations of the former. We illustrate this with a Lennard-Jones solid and a successful application to the phase diagram of hafnia, a complex and interesting oxide.