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cond-mat.mtrl-sci2026
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…
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…