arXiv subjects

Pablo Montero De Hijes

Publications and source records attributed to Pablo Montero De Hijes.

3 recordsLinked to original sources

arXiv2024

Structure of the water/magnetite interface from sum frequency generation experiments and neural network based molecular dynamics simulations

Magnetite, a naturally abundant mineral, frequently interacts with water in both natural settings and various technical applications, making the study of its surface chemistry highly relevant. In this work, we investigate the hydrogen bonding dynamics and the presence of hydroxyl species at the magnetite-water interface using a combination of neural network potential-based molecular dynamics simulations and sum frequency generation vibrational spectroscopy. Our simulations, which involved large water systems, allowed us to identify distinct interfacial species, such as dissociated hydrogen and hydroxide ions formed by water dissociation. Notably, water molecules near the interface exhibited a preference for dipole orientation towards the surface, with bulk-like water behavior only re-emerging beyond 60 Å from the surface. The vibrational spectroscopy results aligned well with the simulations, confirming the presence of a hydrogen bond network in the surface ad-layers. The analysis revealed that surface-adsorbed hydroxyl groups orient their hydrogen atoms towards the water bulk. In contrast, hydrogen-bonded water molecules align with their hydrogen atoms pointing towards the magnetite surface.

cond-mat.mtrl-sci↗

arXiv2024

Unsupervised identification of crystal defects from atomistic potential descriptors

Identifying crystal defects is vital for unraveling the origins of many physical phenomena. Traditionally used order parameters are system-dependent and can be computationally expensive to calculate for long molecular dynamics simulations. Unsupervised algorithms offer an alternative independent of the studied system and can utilize precalculated atomistic potential descriptors from molecular dynamics simulations. We compare the performance of three such algorithms (PCA, UMAP, and PaCMAP) on silicon and water systems. Initially, we evaluate the algorithms for recognizing phases, including crystal polymorphs and the melt, followed by an extension of our analysis to identify interstitials, vacancies, and interfaces. While PCA is found unsuitable for effective classification, it has been shown to be a suitable initialization for UMAP and PaCMAP. Both UMAP and PaCMAP show promising results overall, with PaCMAP proving more robust in classification, except in cases of significant class imbalance, where UMAP performs better. Notably, both algorithms successfully identify nuclei in supercooled water, demonstrating their applicability to ice nucleation in water.

cond-mat.mtrl-sci↗

arXiv2024

Perspective: Atomistic Simulations of Water and Aqueous Systems with Machine Learning Potentials

As the most important solvent, water has been at the center of interest since the advent of computer simulations. While early molecular dynamics and Monte Carlo simulations had to make use of simple model potentials to describe the atomic interactions, accurate ab initio molecular dynamics simulations relying on the first-principles calculation of the energies and forces have opened the way to predictive simulations of aqueous systems. Still, these simulations are very demanding, which prevents the study of complex systems and their properties. Modern machine learning potentials (MLPs) have now reached a mature state, allowing to overcome these limitations by combining the high accuracy of electronic structure calculations with the efficiency of empirical force fields. In this Perspective we give a concise overview about the progress made in the simulation of water and aqueous systems employing MLPs, starting from early work on free molecules and clusters via bulk liquid water to electrolyte solutions and solid-liquid interfaces.

cond-mat.soft↗