arXiv subjects

Sangjin Han

Publications and source records attributed to Sangjin Han.

2 recordsLinked to original sources

arXiv2026

Dimensional Control of Excitonic Interactions in Exfoliated 2D Molecular Crystals

Two-dimensional (2D) materials provide unique opportunities to tailor excited-state properties through reduced dimensionality, altered dielectric screening and layer-dependent structural reconstruction. While such effects have been widely explored in norganic systems, their realization in molecular crystals has been limited by the difficulty of controlling thickness at the atomic scale while preserving crystalline order. Here we show that tetracene and three other molecular crystals can be mechanically exfoliated into mono-, few- or multilayer flakes, while retaining crystalline order. This capability enables new studies of molecular crystals across a well defined thickness range within the same structural organization. Thickness-dependent spectra of these samples reveal how out-of-plane confinement modifies the excited-state energy landscape of tetracene: With decreasing thickness, the Davydov splitting diminishes, the Stokes shift increases, and signatures of more delocalized excitons emerge. Electron diffraction and exciton model-based analyses correlate these trends to changes in molecular packing, intermolecular coupling and dielectric screening. Our results also demonstrate that key features of molecular excitons can be systematically tuned by layer number, extending dimensional control from inorganic 2D materials to molecular crystals.

cond-mat.mtrl-sci↗

arXiv2026

LAMP: Lane-Aligned Motion Primitives for Feasible Trajectory Prediction

Motion forecasting is essential for autonomous driving systems to enable safe decision-making and planning in complex driving scenarios. While existing predictors excel at minimizing standard displacement errors, they often overlook the adherence to lane topology of multimodal predictions, particularly for lower-probability modes. Consequently, predicted trajectories may violate physical and logical constraints, making the prediction set unreliable for safety-critical planning. In this paper, we propose LAMP (Lane-Aligned Motion Primitives), a topology-aware forecasting framework that anchors multimodal prediction to structured motion primitives aligned with lane topology. Specifically, we use a VQ-VAE to learn shape-aware motion primitives as discrete intention queries, capturing spatiotemporal patterns beyond endpoint-based intentions. We further introduce a feasibility-aware intention selector trained with a lane-topology prior for filtering unreachable intention queries, guiding the decoder to prioritize topology-consistent intentions while preserving behavioral diversity. Extensive experiments on the Argoverse 2 dataset demonstrate that LAMP achieves prediction accuracy comparable to state-of-the-art baselines while outperforming them in feasibility and diversity metrics.

cs.RO↗