5 papers · 1 filter
Chemical potentials from structure factors: II. Charged multi-component mixtures
Xiaoyu Wang, Roya Savoj, Musahid Ahmed +1
The chemical potentials of charged multi-component mixtures are central to electrolyte thermodynamics, but remain difficult to compute from atomistic simulations. The S0 method ena…
Chemical potentials from structure factors: I. Neutral multi-component mixtures
Roya Savoj, Xiaoyu Wang, Musahid Ahmed +1
The chemical potentials of multi-component mixtures underlie many physical and chemical phenomena, but remain challenging to compute. The S0 method enables the computation of chemi…
Distilling latent electrostatics from foundation machine learning interatomic potentials
Xiaoyu Wang, Bingqing Cheng
Foundation machine learning interatomic potentials (MLIPs) have enabled atomistic simulations across broad regions of chemical and materials space, but many remain computationally…
Polarizable atomic multipoles for learning long-range electrostatics
Yoonjae Park, Dongjin Kim, Daniel S. King +5
Long-range electrostatics and polarization remain central obstacles to extending machine learning interatomic potentials (MLIPs) to ionic, polar, and interfacial systems. Here we i…
Hydrogen-helium immiscibility boundary in gas-giant planetary interiors from machine-learning molecular dynamics
Xiaoyu Wang, Sebastien Hamel, Bingqing Cheng
The location of the hydrogen-helium (H/He) immiscibility boundary controls whether and where helium rain occurs in giant planets, yet it remains uncertain because high-pressure exp…