ArticlePhysical chemistry chemical physics : PCCP2025
Enhanced configurational sampling methods reveal the importance of molecular stiffness for clustering of oxygenated organic molecules.
Article in Physical chemistry chemical physics : PCCP, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Oxygenated organic molecules (OOMs), formed in the atmosphere by oxidation of volatile organic compounds, have been speculated to take part in new particle formation (NPF). The key parameters for assessing the role of OOMs in NPF are the evaporation rates of small clusters, particularly dimers, derived from quantum chemical binding free energies. The main bottleneck for modelling OOM clusters is the conformational sampling of their high-dimensional potential energy surfaces. In this work, we update previous cluster conformational sampling protocols and apply them to OOM clusters. In addition to tuning cut-off energies and filtering approaches, we force hydrogen bond formation between molecules in the initial sampling, and use metadynamics simulations to search for additional minima. We compute dimer binding free energies for 104 dimers of accretion products formed in isoprene and toluene oxidation, 3 dimers of accretion products from alpha-pinene oxidation, and 36 dimers of polyethylene glycol molecules (PEGs). The binding free energies of the OOM homodimers are almost uncorrelated with the saturation vapour pressures predicted by existing group-contribution approaches. Also, the binding free energies are too high for substantial clustering in typical lower-tropospheric conditions. Using the PEG molecules, we demonstrate that both the weak binding, and the lack of correlation between binding free energies and saturation vapour pressures, are likely caused by intramolecular hydrogen bonding. This self-bonding is dictated by the molecular flexibility, which is ultimately a unimolecular property, and potentially a cost-effectively computable descriptor for assessing the clustering ability of OOMs.
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