The cost can be circumvented through the use of machine learning force fields (MLFFs), which are trained on a set of ab-initio reference calculations, such as DFT, and then used to predict energies and forces on unseen configurations at near ab-initio accuracy. Even with the computational speedup that MLFFs offer, these approaches only capture fast NMR regime dynamic processes at around nano-second time-scales. However, NMR shifts can be effected by changes to the chemical environment at much longer. Indeed, the effect of a dynamic processes on the observed NMR spectra depends significantly on the relationship of the exchange rate, k, and the absolute resonant frequency difference, ∆ν, between the initial and final states. This separates chemical exchange into three regimes: slow exchange (k ≪ ∆ν), where each environment is well resolved in the NMR spectrum; fast exchange (k ≫ ∆ν), where rapid dynamics lead to a complete averaging of individual NMR environments; or intermediate exchange (k ≈ ∆ν) when NMR environment coalescence is observed but averaging is only partial. (At 23.5 T for O, 1 ppm = ∆ν = 136 Hz). In the case of CO 2 binding in MFU-4l, it is clear from static DFT models, Fig. (NMR parameters from Fig. , red star values are taken), that a slow exchange or static regime is inaccurate.