6 papers
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
Dongjin Kim, Daniel S. King, Yoonjae Park +4
Long-range electrostatics and polarization remain central obstacles to extending machine learning interatomic potentials (MLIPs) to ionic, polar, and interfacial systems. Here, we…
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…
Ion-modulated structure, proton transfer, and capacitance in the Pt(111)/water electric double layer
Xiaoyu Wang, Junmin Chen, Zezhu Zeng +3
The electric double layer (EDL) governs electrocatalysis, energy conversion, and storage, yet its atomic structure, capacitance, and reactivity remain elusive. Here we introduce a…
A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials
Dongjin Kim, Xiaoyu Wang, Peichen Zhong +3
Most current machine learning interatomic potentials (MLIPs) rely on short-range approximations, without explicit treatment of long-range electrostatics. To address this, we recent…