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• W3186508157 abstract "• Classification problem suitable for determining the radii of atoms is proposed. • The found atomic radii are strongly correlated with their conventional covalent radii. • A threshold value for regularization strength of the linear classifier is found. • Example of interatomic distances dataset repurposing is given. It is demonstrated that the effective atomic radii suitable for identification of covalent bonding can be revealed from the parameters of machine-learning classifier trained to distinguish between bonded and non-bonded pairs of atoms in 28 710 molecules. The proposed way cardinally differs from the conventional technique of finding the atomic radii by solving a regression problem and approximating the bond lengths by the sum of adjustable parameters. Despite the contrasting approach, the proposed 'machine-learned atomic radii' obtained for H, B, C, N, O, F, Si, P, S, Cl, Ge, As, Se, Br elements are shown to be in close agreement with the conventional 'regression-based' covalent radii. We find this example appealing in demonstrating how some of the concepts peculiar to chemical intuition can be re-discovered by using a suitable machine-learning model. The boundaries between ‘definitely bonded’ and ‘likely bonded’ interatomic distances introduced naturally in the proposed formulation may prove useful for molecular modeling software." @default.
• W3186508157 created "2021-08-02" @default.
• W3186508157 creator A5046370328 @default.
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• W3186508157 date "2021-10-01" @default.
• W3186508157 modified "2023-10-17" @default.
• W3186508157 title "The machine-learned radii of atoms" @default.
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• W3186508157 doi "https://doi.org/10.1016/j.comptc.2021.113389" @default.
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