Unknown

Dataset Information

0

Troubleshooting unstable molecules in chemical space.


ABSTRACT: A key challenge in automated chemical compound space explorations is ensuring veracity in minimum energy geometries-to preserve intended bonding connectivities. We discuss an iterative high-throughput workflow for connectivity preserving geometry optimizations exploiting the nearness between quantum mechanical models. The methodology is benchmarked on the QM9 dataset comprising DFT-level properties of 133 885 small molecules, wherein 3054 have questionable geometric stability. Of these, we successfully troubleshoot 2988 molecules while maintaining a bijective mapping with the Lewis formulae. Our workflow, based on DFT and post-DFT methods, identifies 66 molecules as unstable; 52 contain -NNO-, and the rest are strained due to pyramidal sp2 C. In the curated dataset, we inspect molecules with long C-C bonds and identify ultralong candidates (r > 1.70 Å) supported by topological analysis of electron density. The proposed strategy can aid in minimizing unintended structural rearrangements during quantum chemistry big data generation.

SUBMITTER: Senthil S 

PROVIDER: S-EPMC8179589 | biostudies-literature |

REPOSITORIES: biostudies-literature

Similar Datasets

| S-EPMC10418101 | biostudies-literature
| S-EPMC7854709 | biostudies-literature
| S-EPMC9787733 | biostudies-literature
| S-EPMC6644994 | biostudies-literature
| S-EPMC9044254 | biostudies-literature
| S-EPMC6749418 | biostudies-literature
| S-EPMC2825671 | biostudies-literature
| S-EPMC3641967 | biostudies-literature
| S-EPMC8179434 | biostudies-literature
| S-EPMC8162856 | biostudies-literature