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physics.comp-ph2025
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…
physics.chem-ph2025
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…
cond-mat.mtrl-sci2025★ 2 cited
Machine learning interatomic potential can infer electrical response
Peichen Zhong, Dongjin Kim, Daniel S. King +1
Modeling the response of material and chemical systems to electric fields remains a longstanding challenge. Machine learning interatomic potentials (MLIPs) offer an efficient and s…