1 citations · 1 across the 5 of their papers we have counts for
5 papers
Stochastic Loop Corrections to Belief Propagation for Tensor Network Contraction
Gi Beom Sim, Tae Hyeon Park, Kwang S. Kim +6
Tensor network contraction is a fundamental computational challenge underlying quantum many-body physics, statistical mechanics, and machine learning. Belief propagation (BP) provi…
Bayesian E(3)-Equivariant Interatomic Potential with Iterative Restratification of Many-body Message Passing
Soohaeng Yoo Willow, Tae Hyeon Park, Gi Beom Sim +6
Machine learning potentials (MLPs) have become essential for large-scale atomistic simulations, enabling ab initio-level accuracy with computational efficiency. However, current ML…
Machine Learning Nonadiabatic Dynamics: Eliminating Phase Freedom of Nonadiabatic Couplings with the State-Intraction State-Averaged Spin-Restricted Ensemble-Referenced Kohn-Sham Approach
Sung Wook Moon, Soohaeng Yoo Willow, Tae Hyeon Park +2
Excited-state molecular dynamics (ESMD) simulations near conical intersections (CIs) pose significant challenges when using machine learning potentials (MLPs). Although MLPs have g…
A Sparse Bayesian Committee Machine Potential for Hydrocarbons
Soohaeng Yoo Willow, Gyung Su Kim, Miran Ha +2
Accurate and scalable universal interatomic potentials are key for understanding material properties at the atomic level, a task often hindered by the steep computational scaling.…
Active Sparse Bayesian Committee Machine Potential for Isothermal-Isobaric Molecular Dynamics Simulations
Soohaeng Yoo Willow, Dong Geon Kim, R. Sundheep +3
Recent advancements in machine learning potentials (MLPs) have significantly impacted the fields of chemistry, physics, and biology by enabling large-scale first-principles simulat…