5 papers
Can DFT-trained neural network potentials reproduce structure, solvation, and water-exchange properties in aqueous magnesium solutions?
Sebastian Falkner, Pablo Montero de Hijes, Christoph Dellago +1
Magnesium ions play an essential role in many biological processes but remain challenging to model in biomolecular simulations. Despite considerable scientific effort, classical fo…
Dynamical properties of ab initio water from machine-learning potentials
P. Montero de Hijes, L. Neubeck, G. Kresse +1
We assess the dynamical properties of liquid water predicted by several density functionals using machine-learning interatomic potentials. MACE models were trained for SCAN, RPBE-D…
Comparing the Mechanical and Thermodynamic Definitions of Pressure in Ice Nucleation
Pablo Montero de Hijes, Kaihang Shi, Carlos Vega +1
Crystal nucleation studies using hard-sphere and Lennard-Jones models have shown that the pressure within the nucleus is lower than that in the surrounding liquid. Here, we use the…
Solid-liquid interfacial free energy from computer simulations: Challenges and recent advances
Nicodemo Di Pasquale, Jesus Algaba, Pablo Montero de Hijes +12
The theory of interfacial properties in liquid-liquid or liquid-vapour systems is nearly 200 years old. The advent of computational tools has greatly advanced the field, mainly thr…
Structure of the water/magnetite interface from sum frequency generation experiments and neural network based molecular dynamics simulations
Salvatore Romano, Harsharan Kaur, Moritz Zelenka +5
Magnetite, a naturally abundant mineral, frequently interacts with water in both natural settings and various technical applications, making the study of its surface chemistry high…