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A Neural-Network-Optimized Hydrogen Peroxide Pairwise Additive Model for Classical Simulations

2023; American Chemical Society; Volume: 19; Issue: 13 Linguagem: Inglês

10.1021/acs.jctc.3c00287

1549-9626

Alvaro Ramos Peralta, Gerardo Odriozola,

Computational Drug Discovery Methods

We have developed an all-atom pairwise additive model for hydrogen peroxide using an optimization procedure based on artificial neural networks (ANNs). The model is based on experimental molecular geometry and includes a dihedral potential that hinders the cis-type configuration and allows for crossing the trans one, defined between the planes that have the two oxygen atoms and each hydrogen. The model's parametrization is achieved by training simple ANNs to minimize a target function that measures the differences between various thermodynamic and transport properties and the corresponding experimental values. Finally, we evaluated a range of properties for the optimized model and its mixtures with SPC/E water, including bulk-liquid properties (density, thermal expansion coefficient, adiabatic compressibility, etc.) and properties of systems at equilibrium (vapor and liquid density, vapor pressure and composition, surface tension, etc.). Overall, we obtained good agreement with experimental data.