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

Non-covalent bonding patterns are commonly harvested as a design principle in the field of catalysis, supramolecular chemistry, and functional materials to name a few. Yet, their computational description generally neglects finite temperature and environment effects, which promote competing interactions and alter their static gas-phase properties. Recently, neural network potentials (NNPs) trained on Density Functional Theory (DFT) data have become increasingly popular to simulate molecular phenomena in condensed phase with an accuracy comparable to ab initio methods. To date, most applications have centered on solid-state materials or fairly simple molecules made of a limited number of elements. Herein, we focus on the persistence and strength of chalcogen bonds involving benzotelluradiazole in condensed phase. While the tellurium-containing heteroaromatic molecules are known to exhibit pronounced interactions with anions and lone pairs of different atoms, the relevance of competing intermolecular interactions, notably with the solvent, is complicated to monitor experimentally but also challenging to model at an accurate electronic structure level. Here, we train direct and baselined NNPs to reproduce hybrid DFT energies and forces in order to identify what are the most prevalent non-covalent interactions occurring in a solute-Cl-THF mixture. The simulations in explicit solvent highlight the clear competition with chalcogen bonds formed with the solvent and the short-range directionality of the interaction with direct consequences for the molecular properties in the solution. The comparison with other potentials (e.g., AMOEBA, direct NNP, and continuum solvent model) also demonstrates that baselined NNPs offer a reliable picture of the non--covalent interaction interplay occurring in solution.

非共价键合模式常被作为催化、超分子化学和功能材料等领域的设计原理。然而，其计算描述往往忽视有限温度和环境效应，这些效应促进了竞争性相互作用并改变了其在静态气相中的静态性质。近期，基于密度泛函理论（DFT）数据的神经网络势（NNPs）在模拟凝聚相中的分子现象方面越来越受欢迎，其精度可与从头计算方法相媲美。迄今为止，大多数应用都集中在固态材料或由有限数量元素组成的相对简单的分子上。在此，我们专注于凝聚相中涉及苯并噻二唑的硫族键的持久性和强度。尽管已知含有碲的杂芳族分子会与不同原子的阴离子和孤对电子表现出显著相互作用，但竞争性分子间相互作用（特别是与溶剂的相互作用）的相关性在实验上难以监测，在精确的电子结构水平上建模也颇具挑战。在此，我们训练直接和基准化的NNPs来再现混合DFT的能量和力，以确定在溶质-Cl-THF混合物中最普遍的非共价相互作用。在显式溶剂中的模拟突出了与溶剂形成的硫族键的明显竞争以及与直接相互作用的方向性，这对溶液中的分子性质有直接影响。与其他势能（例如，AMOEBA、直接NNP和连续溶剂模型）的比较也表明，基准化的NNPs为溶液中发生的非共价相互作用相互作用提供了一个可靠的图景。