Verkhov, Valery A. and Meshalkin, Stepan A. and Antonov, Alexander S. and Tupikina, Elena Yu. (2025) A data-driven and quantum chemistry-anchored framework for modeling and classifying carbon-lithium bonding in organolithium aggregates. JOURNAL OF CHEMICAL PHYSICS, 163 (22): 224116. ISSN 0021-9606, 1089-7690
Full text not available from this repository. (Request a copy)Abstract
We present a quantum chemistry-based, data-driven framework for the automated classification of carbon-lithium bonding motifs in archetypal organolithium aggregates. Starting from ab initio potential energy surfaces-guided sampling, we constructed a chemically complete dataset of 81 optimized gas-phase aggregates of methyllithium, t-butyllithium, and phenyllithium (600 C-Li bonds in total) spanning all relevant nuclearities and bonding modes. Twenty geometric, electronic, and topological descriptors obtained from quantum theory of atoms in molecules and Electron Localization Function (ELF) analyses were evaluated via correlation clustering, yielding a minimal, non-redundant, chemically meaningful set dominated by the ELF basin electron population and key bond-path metrics. This reduced descriptor set was used to train two complementary supervised models-a multi-task fully connected neural network and a bootstrap-aggregated decision tree-achieving accuracies of 84% (nucleophile type), 89% (aggregation state), and 84% (bond type) on validation data. Both methods consistently identified ELF-derived descriptors as the most discriminative, enabling physically grounded separation of bonding regimes (2c-2e, multi-center, non-classical, pi-Li) and providing an interpretable, transferable platform for high-throughput bonding analysis in organometallic chemistry.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | AB-INITIO; REACTIVITY; PHENYLLITHIUM; ACCURATE; DESIGN; PATHS |
| Subjects: | 500 Science > 540 Chemistry & allied sciences |
| Divisions: | Chemistry and Pharmacy > Institut für Organische Chemie |
| Depositing User: | Dr. Gernot Deinzer |
| Date Deposited: | 18 Jun 2026 05:23 |
| Last Modified: | 18 Jun 2026 05:23 |
| URI: | https://pred.uni-regensburg.de/id/eprint/66499 |
Actions (login required)
 |
View Item |
