23 citations · 28 across the 13 of their papers we have counts for
13 papers
GW and Bethe-Salpeter Theory for Molecular Polaritons, Quasiparticles, and Excitons
Soohaeng Yoo Willow, Gi Beom Sim, Tae Hyeon Park +6
The electron self-energy is central to quasiparticle theory, yet how an optical cavity enters it remains unclear. We address this question for a molecule in a single-mode cavity us…
Sustainability-Aware Discovery of Dual-Atom Hydrogen Evolution Catalysts via a Unified Machine Learning Framework for Activity and Carbon Cost
Yanmei Zang, Hyun Gyu Park, Gi Beom Sim +8
Catalyst discovery for green hydrogen has optimized activity alone, deferring economic and environmental costs to stages where they disqualify otherwise promising candidates. Here…
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…
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…
A Bayesian Committee Machine Potential for Oxygen-containing Organic Compounds
Seungwon Kim, D. ChangMo Yang, Soohaeng Yoo Willow +1
Understanding the pivotal role of oxygen-containing organic compounds in serving as an energy source for living organisms and contributing to protein formation is crucial in the fi…
A Bayesian Committee Machine Potential for Organic Nitrogen Compounds
Hyun Gyu Park, Soohaeng Yoo Willow, D. ChangMo Yang +1
Large-scale computer simulations of chemical atoms are used in a wide range of applications, including batteries, drugs, and more. However, there is a problem with efficiency as it…
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…
Learned Mappings for Targeted Free Energy Perturbation between Peptide Conformations
Soohaeng Yoo Willow, Lulu Kang, David D. L. Minh
Targeted free energy perturbation uses an invertible mapping to promote configuration space overlap and the convergence of free energy estimates. However, developing suitable mappi…
On the Polarization of Ligands by Proteins
Soohaeng Yoo Willow, Bing Xie, Jason Lawrence +2
Although ligand-binding sites in many proteins contain a high number density of charged side chains that can polarize small organic molecules and influence binding, the magnitude o…
The freezing tendency towards 4-coordinated amorphous network causes increase in heat capacity of supercooled Stillinger-Weber silicon
Pankaj A. Apte, Nandlal Pingua, Arvind Kumar Gautam +4
The supercooled liquid silicon, modeled by Stillinger-Weber potential, shows anomalous increase in heat capacity , with a maximum value close to 1060 K at zero pressure.…