Journal article
Assessing the persistence of chalcogen bonds in solution with neural network potentials
- Abstract:
- 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 a 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
- Publication status:
- Published
- Peer review status:
- Peer reviewed
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(Version of record, xml, 1.5KB, Terms of use)
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- Publisher copy:
- 10.1063/5.0085153
Authors
+ Schweizerischer Nationalfonds Zur Förderung der Wissenschaftlichen Forschung
More from this funder
- Funder identifier:
- 10.13039/501100001711
- Grant:
- 200020_175496
+ H2020 European Research Council
More from this funder
- Funder identifier:
- 10.13039/100010663
- Grant:
- 817977
- Publisher:
- American Institute of Physics
- Journal:
- The Journal of Chemical Physics More from this journal
- Volume:
- 156
- Issue:
- 15
- Pages:
- 154112-154112
- Article number:
- 154112
- Publication date:
- 2022-03-29
- DOI:
- EISSN:
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1089-7690
- ISSN:
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0021-9606
- Language:
-
English
- Keywords:
- Pubs id:
-
2001210
- Local pid:
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pubs:2001210
- Source identifiers:
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W4220986587
- Deposit date:
-
2026-06-11
- ARK identifier:
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Terms of use
- Copyright date:
- 2022
- Licence:
- CC Attribution (CC BY)
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