arXiv subjects

Jay Huang

Publications and source records attributed to Jay Huang.

2 recordsLinked to original sources

arXiv2025

Super-diffusion of Photoexcited Carriers in Topological Insulator Nanoribbons

Understanding the ultrafast dynamics and transport of photoexcited carriers in topological insulators is crucial for the optical manipulation of spins and may shed light on the nature of topological excitons. Here we investigate bulk-insulating Sb-doped $\mathrm{Bi_2Se_3}$ nanoribbons via ultrafast transient photovoltage microscopy. The probe-pulse-induced photovoltage is substantially suppressed by a pump pulse. Recovery time increases from 50 to 1600 picoseconds as the pump fluence increases. We found that the diffusivity of photoexcited carriers increases significantly at lower carrier concentrations, up to 800 cm$^2$/s at 21 K, two to three orders of magnitude higher than that of band-edge carriers. Remarkably, the photoexcited carriers travel up to 10 $\mu$m for hundreds of picoseconds at this high diffusivity. The diffusivity peaks in intrinsic devices and is reduced at high temperatures. We discuss the possible mechanisms of long-ranged super-diffusion in the frames of hot carriers and exciton condensation.

cond-mat.mes-hall↗

arXiv2023

NodeCoder: a graph-based machine learning platform to predict active sites of modeled protein structures

While accurate protein structure predictions are now available for nearly every observed protein sequence, predicted structures lack much of the functional context offered by experimental structure determination. We address this gap with NodeCoder, a task-independent platform that maps residue-based datasets onto 3D protein structures, embeds the resulting structural feature into a contact network, and models residue classification tasks with a Graph Convolutional Network (GCN). We demonstrate the versatility of this strategy by modeling six separate tasks, with some labels derived from other experimental structure studies (ligand, peptide, ion, and nucleic acid binding sites) and other labels derived from annotation databases (post-translational modification and transmembrane regions). Moreover, A NodeCoder model trained to identify ligand binding site residues was able to outperform P2Rank, a widely-used software developed specifically for ligand binding site detection. NodeCoder is available as an open-source python package at https://pypi.org/project/NodeCoder/.

q-bio.QM↗