Atomic Neural Network for Calculation of Solvation Free Energies in Organic Solvents
This paper introduces AtomicESE, an artificial neural network for calculating solvation-free energies ΔG°solv of molecules in organic solvents. AtomicESE calculates ΔG°solv by summing atomic contributions, each evaluated by a dense neural network. This atomic network uses 13 physically relevant inpu...
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2025 |
| País: | España |
| Institución: | Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya) |
| Repositorio: | Recercat. Dipósit de la Recerca de Catalunya |
| OAI Identifier: | oai:recercat.cat:10256/27275 |
| Acceso en línea: | http://hdl.handle.net/10256/27275 |
| Access Level: | acceso abierto |
| Palabra clave: | Solvatació Solvation |
| Sumario: | This paper introduces AtomicESE, an artificial neural network for calculating solvation-free energies ΔG°solv of molecules in organic solvents. AtomicESE calculates ΔG°solv by summing atomic contributions, each evaluated by a dense neural network. This atomic network uses 13 physically relevant input features, comprising six local atomic features, two global charge-related molecular properties, and five solvent-specific properties. For neutral solutes, AtomicESE achieves an average RMSE below 0.6 kcal/mol, demonstrating strong performance across all solvent classes, with particularly high accuracy for aromatic, haloaromatic, alkane, and ketone solvents. AtomicESE also works reliably for ionic solutes |
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