Overall, the vsm can recapitulate the experimental nitrile spectra with high accuracy. This demonstrates that our HB geometry-dependent model is not only robust in minimally fluctuating settings, but also in (quickly) fluctuating solvent or protein environments when dynamical effects are considered. More specifically, in the cases we tested with fluctuating HBs, the geometry dependent values of 〈∆𝜈̅ 𝐻𝐵, (𝑑, 𝜃)〉 = 23.8 -26.3 cm -1 (Fig. ) are reduced by a factor of roughly 2 to ∆𝜈̅ 𝐻𝐵,𝑜𝑏𝑠 ≈ ∆𝜈̅ 𝐻𝐵,𝑣𝑠𝑚 = 11.8 -15.2 cm -1 (Fig. ). This reduction by a factor of 2 is what is expected for the simplest case when nitrile protic/aprotic sub-populations are interconverting with similarly fast exchange rates such that the geometry dependent value 〈∆𝜈̅ 𝐻𝐵, (𝑑, 𝜃)〉 will be averaged with 0 cm -1 (i.e., the blueshift for the non-H-bonded fraction). This exercise makes clear that knowledge of the dynamics experienced by a nitrile is key to prevent erroneous assessments of the HB geometry based on ∆𝜈̅ 𝐻𝐵,𝑜𝑏𝑠 alone: such dynamics can be evaluated using temperature dependent or two-dimensional IR experiments. To summarize the evaluation of our models for HB blueshifts, we correlated the experimental and predicted values for ∆𝜈̅ 𝐻𝐵 in Fig. . We find that calculating 〈∆𝜈̅ 𝐻𝐵, (𝑑, 𝜃)〉 from our HB geometry-dependent model in eq. 7 works very well for rigid HBs in F92oCNF and MOFs, implying it is possible to extract information on HB geometry directly from HB blueshifts. For fluctuating HBs like F28oCNF and oTN in solvents, HB dynamics have to be considered, as described above: when they are, an excellent agreement between observed and modelled HB blueshifts is obtained (R 2 = 0.95). blueshifts; when the appropriate model is used, the correlation is excellent (R 2 = 0.95). The black diagonal is the perfect correlation with a slope of 1. Black data points for MOFs are taken from ref. .