Anthony Val Canillas Camposano

• Camposano, Anthony Val Canillas; Sveinsson, Henrik Andersen & Malthe-Sørenssen, Anders (2024). Machine Learning an All-Atom Empirical Force Field for a Water-Silica System. Show summary

  Molecular modeling of the interaction between silica and water provides a better understanding of how water affects rocks and glasses at the nanoscale. Here, we parametrize a new classical dissociative force field for the water–silica system. We create a classical force field capable of reactive interactions between all chemical species in the water–silica system, using the same functional form by Vashishta et al. to describe bulk water, silica, and silica–water interactions. Using a hierarchical genetic algorithm as a global optimizer and the Nelder-Mead simplex algorithm as a local optimizer, we parameterized the Vashishta force field to reproduce the density, transport properties, and vapor-liquid properties of water. We then used the same method to obtain water–silica interaction parameters to reproduce gas phase orthosilicic geometry and silica surface properties such as silanol concentration, heat of immersion, and the wettability angle of water on a silica surface. For oxygen–silicon interactions in silica, we reuse an existing Vashishta potential parameter set that has been parameterized to reproduce bulk mechanical behavior, surface properties, and fracture properties. Our developed force field can be used to study processes such as dissolution, friction, and stress corrosion fracture in the presence of water over a wide range of thermodynamic conditions for larger-scale and longer-scale simulations.

• Camposano, Anthony Val Canillas; Sveinsson, Henrik Andersen & Malthe-Sørenssen, Anders (2023). Tuning Vashishta Potential for Water.

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