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; of which 3,054 have questionable geometric stability. We successfully troubleshoot 2,988 molecules and ensure a bijective mapping between desired Lewis formulae and final geometries. Our workflow, based on DFT and post-DFT methods, identifies 66 molecules as unstable; 52 contain −NNO−, the rest are strained due to pyramidal sp2 C. In the curated dataset, we inspect molecules with long CC bonds and identify ultralong contestants (r>1.70~\AA{}) supported by topological analysis of electron density. We hope the proposed strategy to play a role in big data quantum chemistry initiatives.
@article{arxiv.2010.02635,
title = {Troubleshooting Unstable Molecules in Chemical Space},
author = {Salini Senthil and Sabyasachi Chakraborty and Raghunathan Ramakrishnan},
journal= {arXiv preprint arXiv:2010.02635},
year = {2020}
}