Troubleshooting unstable molecules in chemical space

• Published in: Chemical science (Cambridge) Vol. 12; no. 15; pp. 5566 - 5573
• Main Authors: Senthil, Salini, Chakraborty, Sabyasachi, Ramakrishnan, Raghunathan
• Format: Journal Article
• Language: English
• Published: Cambridge Royal Society of Chemistry 21.04.2021; The Royal Society of Chemistry
• Subjects: Chemical compounds; Chemistry; Covalent bonds; Datasets; Electron density; Iterative methods; Quantum chemistry; Quantum mechanics; Troubleshooting; Workflow
• ISSN: 2041-6520; 2041-6539
• DOI: 10.1039/d0sc05591c

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 sp 2 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. A high-throughput workflow for connectivity preserving geometry optimization minimizes unintended structural rearrangements during quantum chemistry big data generation.