237 citations · 2.5k across the 60 of their papers we have counts for
61 papers
Ion-Specific Anomalous Water Diffusion in Aqueous Electrolytes: A Machine-Learned Many-Body Force Field Study with MACE
Massimo Ciacchi, Ilnur Saitov, Nico Di Fonte +2
The dynamics of water in electrolyte solutions exhibits a striking, ion-specific anomaly: the diffusion coefficient of water is enhanced relative to the neat liquid in chaotropic C…
The liquid-liquid phase transition of hydrogen and its critical point: Analysis from ab initio simulation and a machine-learned potential
Mathieu Istas, Scott Jensen, Yubo Yang +3
We simulate high-pressure hydrogen in its liquid phase close to molecular dissociation using a machine-learned interatomic potential. The model is trained with density functional t…
High temperature melting of dense molecular hydrogen from machine-learning interatomic potentials trained on quantum Monte Carlo
Shubhang Goswami, Scott Jensen, Yubo Yang +3
We present results and discuss methods for computing the melting temperature of dense molecular hydrogen using a machine learned model trained on quantum Monte Carlo data. In this…
First principles simulations of dense hydrogen
Michael Bonitz, Jan Vorberger, Mandy Bethkenhagen +24
Accurate knowledge of the properties of hydrogen at high compression is crucial for astrophysics (e.g. planetary and stellar interiors, brown dwarfs, atmosphere of compact stars) a…
Electronic excitation spectra of molecular hydrogen in Phase I from Quantum Monte Carlo and Many-Body perturbation methods
Vitaly Gorelov, Markus Holzmann, David M. Ceperley +1
We study the electronic excitation spectra in solid molecular hydrogen (phase I) at ambient temperature and 5-90 GPa pressures using Quantum Monte Carlo methods and Many-Body Pertu…
Training models using forces computed by stochastic electronic structure methods
David M. Ceperley, Scott Jensen, Yubo Yang +3
Quantum Monte Carlo (QMC) can play a very important role in generating accurate data needed for constructing potential energy surfaces. We argue that QMC has advantages in terms of…