9 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…
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 planets
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…
Long-range electrostatics for machine learning interatomic potentials is easier than we thought
Dongjin Kim, Bingqing Cheng
The lack of long-range electrostatics is a key limitation of modern machine learning interatomic potentials (MLIPs), hindering reliable applications to interfaces, charge-transfer…
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…
Foundation Models for Atomistic Simulation of Chemistry and Materials
Eric C. -Y. Yuan, Yunsheng Liu, Junmin Chen +11
Given the power of large language and large vision models, it is of profound and fundamental interest to ask if a foundational model based on data and parameter scaling laws and pr…