Berlin 2024 – scientific programme

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O: Fachverband Oberflächenphysik

O 32: Poster: Solid-Liquid Interfaces

O 32.1: Poster

Tuesday, March 19, 2024, 18:00–20:00, Poster C

Molecular dynamics simulations of dicalcium silicate - water interfaces by High-Dimensional Neural Network Potentials — •Bernadeta Prus^{1, 2} and Jörg Behler^{1, 2} — ¹Theoretische Chemie II, Ruhr-Universität Bochum, Germany — ²Research Center Chemical Sciences and Sustainability, Research Alliance Ruhr, Germany

In recent years, there has been a growing utilization of High-Dimensional Neural Network Potentials (HDNNP) based on Density Functional Theory (DFT) calculations to enable high-quality molecular dynamics simulations of water interactions with a variety of solid minerals. This study explores the case of dicalcium silicate (Ca₂SiO₄), which is important in many fields. This chemical compound exhibits five polymorphic states, the low-temperature polymorph, denoted as γ, is naturally occurring in the Calcio-olivine mineral. The primary focus of this research is to compare the reactivity in contact with water of different terminations along the [010] surfaces of the γ polymorph of dicalcium silicate. The chosen computational approach allows the development of a single HDNNP suitable for molecular dynamics simulations for all distinct interfaces significantly reducing the computational time.

Keywords: Machine learning potentials; Molecular dynamics; Water; Interfaces