DPG Phi
Verhandlungen
Verhandlungen
DPG

Berlin 2024 – wissenschaftliches Programm

Bereiche | Tage | Auswahl | Suche | Aktualisierungen | Downloads | Hilfe

O: Fachverband Oberflächenphysik

O 32: Poster: Solid-Liquid Interfaces

O 32.1: Poster

Dienstag, 19. März 2024, 18:00–20:00, Poster C

Molecular dynamics simulations of dicalcium silicate - water interfaces by High-Dimensional Neural Network Potentials — •Bernadeta Prus1, 2 and Jörg Behler1, 21Theoretische 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

100% | Mobil-Ansicht | English Version | Kontakt/Impressum/Datenschutz
DPG-Physik > DPG-Verhandlungen > 2024 > Berlin