4 citations · 9 across the 9 of their papers we have counts for
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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…
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…