arXiv subjects

YeongKyu Lee

Publications and source records attributed to YeongKyu Lee.

4 recordsLinked to original sources

arXiv2026

Physical Constraints on the Rhythmicity of the Biological Clock

Circadian rhythms in living organisms are temporal orders emerging from biochemical circuits driven out of equilibrium. Here, considering the KaiABC system, a minimal model in the synthetic biology, we study how the oscillation emerges from the circuit made of three Kai proteins and ATP alone. The phase diagram constructed in terms of KaiC and KaiA concentrations reveals a narrowly bounded oscillatory phase, which naturally explains arrhythmia upon protein over-expression. As dictated by the cost-precision trade-offs of the thermodynamic uncertainty relations, the presence of intrinsic noise, amplified in small systems, demands higher free energy cost to achieve greater rhythmic precision. The cost-minimizing condition within the oscillatory phase is found to generate $\sim$21-hr rhythm, which is entrained to 24-hr environmental signals as long as the forcing amplitude is greater than $\sim 10$ \% of the metabolic rate. An optimal level of intrinsic noise can also induce oscillations even beyond the Hopf bifurcation, effectively expanding the oscillatory phase. Our study clarifies how the physical factors, such as regulatory mechanism, energy cost, and stochastic noise contribute to the operation of biological clocks.

cond-mat.stat-mech↗

arXiv2023

Electric Double Layer from Phase Demixing Reinforced by Strong Coupling Electrostatics

Ionic liquids (ILs) are appealing electrolytes for their favorable physicochemical properties. However, despite their longstanding use, understanding the capacitive behavior of ILs remains challenging. This is largely due to the formation of a non-conventional electric double layer (EDL) at the electrode-electrolyte interface. This study shows that the short-range Yukawa interactions, representing the large anisotropically charged ILs, demix IL to create a spontaneous surface charge separation, which is reinforced by the strongly coupled charge interaction. The properties of the condensed layer, the onset of charge separation, and the rise of overscreening and crowding critically depend on the asymmetry of Yukawa interactions.

cond-mat.soft↗

arXiv2023

Anomalous Diffusion of Lithium-Anion Clusters in Ionic Liquids

Lithium-ion transport is significantly retarded in ionic liquids (ILs). In this work, we performed extensive molecular dynamics (MD) simulations to mimic the kinetics of lithium ions in ILs using [\emph{N}-methyl-\emph{N}-propylpyrrolidium (pyr$_{13}$)][bis(trifluoromethanesulfonyl)imide (Ntf$_{2}$)] with added LiNtf$_{2}$ salt. And we analyzed their transport, developing a two-state model and comparing it to the machine learning-identified states. The transport of lithium ions involves local shell exchanges of the Ntf$_{2}$ in the medium. We calculated train size distributions over various time scales. The train size distribution decays as a power law, representing non-Poissonian bursty shell exchanges. We analyzed the non-Poissonian processes of lithium ions transport as a two-state (soft and hard) model. We analytically calculated the transition probability of the two-state model, which fits well to the lifetime autocorrelation functions of LiNtf$_{2}$ shells. To identify two states, we introduced the graph neutral network incorporating local molecular structure. The results reveal that the shell-soft state mainly contributes to the transport of the lithium ions, and their contribution is more important in low temperatures. Hence, it is the key for enhanced lithium ion transport to increase the fraction of the shell-soft state.

physics.comp-ph↗

arXiv2023

Renormalization Group-Motivated Learning

We introduce an RG-inspired coarse-graining for extracting the collective features of data. The key to successful coarse-graining lies in finding appropriate pairs of data sets. We coarse-grain the two closest data in a regular real-space RG in a lattice while considers the overall information loss in momentum-space RG. Here we compromise the two measures for the non-spatial data set. For weakly correlated data close to Gaussian, we use the correlation of data as a metric for the proximity of data points, but minimize an overall projection error for optimal coarse-graining steps. It compresses the data to maximize the correlation between the two data points to be compressed while minimizing the correlation between the paired data and other data points. We show that this approach can effectively reduce the dimensionality of the data while preserving the essential features. We extend our method to incorporate non-linear features by replacing correlation measures with mutual information. This results in an information-bottleneck-like trade-off: maximally compress the data while preserving the information among the compressed data and the rest. Indeed, our approach can be interpreted as an exact form of information-bottleneck-like trade off near linear data. We examine our method with random Gaussian data and the Ising model to demonstrate its validity and apply glass systems. Our approach has potential applications in various fields, including machine learning and statistical physics.

physics.data-an↗