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

Veronika Jurásková1, Frédéric Célerse1, Rubén Laplaza1,2, Clémence Corminboeuf1,2,3*

1 Laboratory for Computational Molecular Design (LCMD), Institute of Chemical Sciences and Engineering, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, CH-1015, Switzerland.

2 National Center for Competence in Research-Catalysis (NCCR-Catalysis), École Polytechnique Fédérale de Lausanne, CH-1015 Lausanne, Switzerland.

3 National Centre for Computational Design and Discovery of Novel Materials (MARVEL), École Polytechnique Fédérale de Lausanne, CH-1015 Lausanne, Switzerland.

* Corresponding authors emails: clemence.corminboeuf@epfl.ch
DOI10.24435/materialscloud:90-vd [version v1]

Publication date: Mar 16, 2022

How to cite this record

Veronika Jurásková, Frédéric Célerse, Rubén Laplaza, Clémence Corminboeuf, Assessing the persistence of chalcogen bonds in solution with neural network potentials, Materials Cloud Archive 2022.42 (2022), doi: 10.24435/materialscloud:90-vd.


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.

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File name Size Description
3.4 KiB Example input to i-PI to run MD using MTS approach with baselined and direct NNP
2.3 KiB Example LAMMPS input to run MD with NNP using i-PI
117.2 MiB Parameters for the baselined NNPs in n2p2 format
779.0 KiB Parameters for the direct NNPs in n2p2 format
1.7 KiB Input for PBE computations in cp2k
3.3 KiB Input for PBE0 computations in cp2k
157 Bytes Example PLUMED input for umbrella sampling


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chalcogen bonding in solution neural network potentials molecular dynamics non-covalent interactions machine learning EPFL ERC SNSF

Version history:

2022.42 (version v1) [This version] Mar 16, 2022 DOI10.24435/materialscloud:90-vd