222 citations · 227 across the 7 of their papers we have counts for
Showing 2024Show all
2 papers · 1 filter
cond-mat.mtrl-sci2024
Data-efficient multi-fidelity training for high-fidelity machine learning interatomic potentials
Jaesun Kim, Jisu Kim, Jaehoon Kim +4
Machine learning interatomic potentials (MLIPs) are used to estimate potential energy surfaces (PES) from ab initio calculations, providing near quantum-level accuracy with reduced…
cond-mat.mtrl-sci2024★ 222 cited
Scalable Parallel Algorithm for Graph Neural Network Interatomic Potentials in Molecular Dynamics Simulations
Yutack Park, Jaesun Kim, Seungwoo Hwang +1
Message-passing graph neural network interatomic potentials (GNN-IPs), particularly those with equivariant representations such as NequIP, are attracting significant attention due…