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Non-covalent bonding patterns are commonly harvested as a design principle inthe field of catalysis, supramolecular chemistry and functional materials toname a few. Yet, their computational description generally neglects finitetemperature and environment effects, which promote competing interactions andalter their static gas-phase properties. Recently, neural network potentials(NNPs) trained on Density Functional Theory (DFT) data have become increasinglypopular to simulate molecular phenomena in condensed phase with an accuracycomparable to ab initio methods. To date, most applications have centered onsolid-state materials or fairly simple molecules made of a limited number ofelements. Herein, we focus on the persistence and strength of chalcogen bondsinvolving a benzotelluradiazole in condensed phase. While thetellurium-containing heteroaromatic molecules are known to exhibit pronouncedinteractions with anions and lone pairs of different atoms, the relevance ofcompeting intermolecular interactions, notably with the solvent, is complicatedto monitor experimentally but also challenging to model at an accurateelectronic structure level. Here, we train direct and baselined NNPs toreproduce hybrid DFT energies and forces in order to identify what are the mostprevalent non-covalent interactions occurring in a solute-Cl$^-$-THF mixture.The simulations in explicit solvent highlight the clear competition withchalcogen bonds formed with the solvent and the short-range directionality ofthe interaction with direct consequences for the molecular properties in thesolution. The comparison with other potentials (e.g., AMOEBA, direct NNP andcontinuum solvent model) also demonstrates that baselined NNPs offer a reliablepicture of the non-covalent interaction interplay occurring in solution.

Assessing the persistence of chalcogen bonds in solution with neural network potentials