Wenting Wu | Searcharxiv

arXiv2025

Explosive growth of bistability in a cavity magnonic system

We conduct a theoretical investigation into explosive growth of bistability in a cavity magnonic system incorporating magnetic nonlinearity. In this system, the coupling between the magnon and photon generates the cavity magnon polaritons. When driving the photon-like polariton mode, the bistability can undergo a sudden transition with the increase of the driving power, resulting in an explosive growth of the bistable region by several times. Conversely, driving the magnon-like polariton mode only gives rise to normal bistability. This depends on whether the minimum driving power required to generate the bistability is non-monotonic with respect to the driving frequency. In addition, despite driving only the photon-like polariton mode, the photon- and magnon-like polariton modes can show simultaneous explosive growth of the bistability in microwave transmission, owing to the light-matter interaction. Our research sheds light on the hidden side of the nonlinear cavity magnonic system and provides a potential application for cavity spintronic devices founded on this novel feature.

cond-mat.mes-hall↗

arXiv2025

BioChemInsight: An Online Platform for Automated Extraction of Chemical Structures and Activity Data from Patents

The automated extraction of chemical structures and their corresponding bioactivity data is essential for accelerating drug discovery and enabling data-driven research. Current optical chemical structure recognition tools lack the capability to autonomously link molecular structures with their bioactivity profiles, posing a significant bottleneck in structure-activity relationship analysis. To address this, we present BioChemInsight, an open-source pipeline that integrates DECIMER Segmentation with MolNexTR for chemical structure recognition, GLM-4.5V for compound identifier association, and PaddleOCR combined with GLM-4.6 for bioactivity extraction and unit normalization. We evaluated BioChemInsight on 181 patents covering 15 therapeutic targets. The system achieved an average extraction accuracy of above 90% across three key tasks: chemical structure recognition, bioactivity data extraction, and compound identifier association. Our analysis indicates that the chemical space covered by patents is largely complementary to that contained in established public database ChEMBL. Consequently, by enabling systematic patent mining, BioChemInsight provides access to chemical information underrepresented in ChEMBL. This capability expands the landscape of explorable compound-target interactions, enriches the data foundation for quantitative structure-activity relationship modeling and targeted screening, and reduces data preprocessing time from weeks to hours. BioChemInsight is available at https://github.com/dahuilangda/BioChemInsight.

q-bio.QM↗