Rowan's ion-mobility-mass-spectrometry workflow predicts rotationally averaged collision cross sections (CCS) from molecular structures in a way that is conformationally aware, protonation-aware, and grounded in an explicit scattering calculation.
The workflow starts from a single input molecule which is checked to ensure it corresponds to exactly one connected species; mixtures are rejected to avoid ambiguous CCS assignments. Next, the workflow constructs a realistic ensemble of 3D geometries in the appropriate charge state. If "protonate" mode is enabled, it first enumerates protomers using a pKa-based microstate generator, building candidate protonation states for known protonatable elements. For each protomer, it runs a conformer search tuned for ion mobility applications, typically combining CREST-style sampling with rapid quantum or semiempirical refinement. The minimum energy across all protonation/conformer combinations is tracked, and the protomer–conformer ensemble with the lowest accessible energy is selected as the basis for CCS prediction. If protonation is disabled, the workflow instead runs this geometry generation directly on the provided charge state. In either case, the result is a conformer ensemble with associated single-point energies computed at a consistent level of theory.
From this ensemble, the workflow identifies which conformers actually matter: Boltzmann weights are computed from the relative energies, and only conformers whose weights exceed a small cutoff are kept; if none qualify, the single most favorable conformer is retained. For each selected conformer, the workflow then performs an explicit CCS calculation using Rowan's modified version of CoSIMS. Before calling CoSIMS, it computes gas-phase partial charges using AIMNet2 to provide a more realistic description of long-range interactions, especially for ions. The molecular geometry, atomic numbers, charges, and total charge are written into an .mfj file, and CoSIMS is then run inside a temporary directory with a bounded runtime and a small number of automatic retries. Its text output is parsed to extract the mean CCS and associated standard deviation for that conformer; any runs that fail or time out are logged and marked with NaN values instead of silently contaminating the average.
Finally, the per-conformer CCS values are combined into an overall prediction. Only conformers with finite CCS and uncertainty estimates are used; their Boltzmann weights are renormalized, and the workflow computes a weighted mean CCS and a total standard deviation that accounts for both the intrinsic uncertainty of each CoSIMS estimate and the spread in CCS values across the ensemble. These aggregated quantities—along with the underlying conformer-level CCS values, uncertainties, and weights—are returned to the end user.