2 citations · 5 across the 4 of their papers we have counts for
4 papers
Cross-functional transferability in universal machine learning interatomic potentials
Xu Huang, Bowen Deng, Peichen Zhong +3
The rapid development of universal machine learning interatomic potentials (uMLIPs) has demonstrated the possibility for generalizable learning of the universal potential energy su…
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…
Learning charges and long-range interactions from energies and forces
Dongjin Kim, Daniel S. King, Peichen Zhong +1
Accurate modeling of long-range forces is critical in atomistic simulations, as they play a central role in determining the properties of materials and chemical systems. However, s…
Grand-canonical Monte-Carlo simulation methods for charge-decorated cluster expansions
Fengyu Xie, Peichen Zhong, Luis Barroso-Luque +2
Monte-Carlo sampling of lattice model Hamiltonians is a well-established technique in statistical mechanics for studying the configurational entropy of crystalline materials. When…