Dong Geon Kim | Searcharxiv

arXiv2026

Experimental and theoretical studies of hyperfine structures in $^{21}$Na

We measured the hyperfine structure constants, $A(3s^2S_{1/2})$ and $A(3p^2P_{1/2})$, of the neutron-deficient isotope $^{21}\text{Na}$ using CLaSsy, a setup dedicated to collinear laser spectroscopy at RAON. The hyperfine structure constants of $^{21}\text{Na}$ were measured to be $103.6(10)_{\mathrm{stat}}(9)_{\mathrm{syst}}$ MHz for $A(3p^2P_{1/2})$ and $954.9(11)_{\mathrm{stat}}(25)_{\mathrm{syst}}$ MHz for $A(3s^2S_{1/2})$. A systematic comparison with the state-of-the-art ab-initio relativistic coupled cluster calculations shows the role of higher-order correlation effects such as triple excitations in $^{21}$Na. Furthermore, the measurement demonstrates a capability of the CLaSsy setup to conduct collinear laser spectroscopy experiments with a radioactive beam.

physics.atom-ph↗

arXiv2024

Active Sparse Bayesian Committee Machine Potential for Isothermal-Isobaric Molecular Dynamics Simulations

Recent advancements in machine learning potentials (MLPs) have significantly impacted the fields of chemistry, physics, and biology by enabling large-scale first-principles simulations. Among different machine learning approaches, kernel-based MLPs distinguish themselves through their ability to handle small datasets, quantify uncertainties, and minimize over-fitting. Nevertheless, their extensive computational requirements present considerable challenges. To alleviate these, sparsification methods have been developed, aiming to reduce computational scaling without compromising accuracy. In the context of isothermal and isobaric ML molecular dynamics (MD) simulations, achieving precise pressure estimation is crucial for reproducing reliable system behavior under constant pressure. Despite progress, sparse kernel MLPs struggle with precise pressure prediction. Here, we introduce a virial kernel function that significantly enhances pressure estimation accuracy of MLPs. Additionally, we propose the active sparse Bayesian committee machine (BCM) potential, an on-the-fly MLP architecture that aggregates local sparse Gaussian process regression (SGPR) MLPs. The sparse BCM potential overcomes the steep computational scaling with the kernel size, and a predefined restriction on the size of kernel allows for a fast and efficient on-the-fly training. Our advancements facilitate accurate and computationally efficient machine learning-enhanced MD (MLMD) simulations across diverse systems, including ice-liquid coexisting phases, \ce{Li10Ge(PS6)2} lithium solid electrolyte, and high-pressure liquid boron nitride.

cond-mat.soft↗