5 papers
Accelerated Discovery of Nitrogen-Coordinated Dual-Atom Hydrogen Evolution Reaction Electrocatalysts via Machine Learning Potentials
Yanmei Zang, Hyun Gyu Park, Gi Beom Sim +7
The hydrogen evolution reaction (HER) is central to sustainable hydrogen production, and nitrogen coordinated dual atom catalysts (DACs) offer a promising route to noble metal acti…
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…
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…
Hybrid Quantum--Classical Machine Learning Potential with Variational Quantum Circuits
Soohaeng Yoo Willow, D. ChangMo Yang, Chang Woo Myung
Quantum algorithms for simulating large and complex molecular systems are still in their infancy, and surpassing state-of-the-art classical techniques remains an ever-receding goal…
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…