arXiv subjects

Tianci Song

Publications and source records attributed to Tianci Song.

2 recordsLinked to original sources

arXiv2026

Contrasting $\Gamma$- and K-Valley Moir\'e Physics in Twisted Monolayer/Bilayer WSe$_2$

Electronic orbital character plays a central role in determining electronic correlations, spin-orbit coupling, dimensionality, and ultimately the quantum phases of condensed-matter systems. Two-dimensional moir\'e materials have emerged as highly tunable platforms for exploring correlated phenomena, but the role of orbital degrees of freedom remains largely unexplored. Here, we identify twisted monolayer/bilayer WSe$_2$ as a platform in which displacement-field tuning enables moir\'e physics to be realized in both the $K$ and $\Gamma$ valleys. The distinct orbital characters of these valleys give rise to contrasting correlated phases at moir\'e filling factors $\nu=1$ and $\nu=1/3$. At $\nu=1$, the $K$-valley state is a weak insulator, consistent with an antiferromagnetic state near a van Hove singularity in the intermediate-coupling regime, similar to that observed in twisted bilayer WSe$_2$. In contrast, the $\Gamma$-valley state exhibits a pronounced Pomeranchuk effect, consistent with proximity to a Mott transition. At $\nu=1/3$, the $K$ valley hosts a robust generalized Wigner crystal, whereas the $\Gamma$-valley state lies near the crystallization boundary and again exhibits a Pomeranchuk effect, with localization enhanced by increasing temperature or magnetic field. Our work highlights the importance of orbital character in defining quantum phases in moir\'e systems, and identify the $\Gamma$ valley as a promising platform for exploring correlated phenomena near quantum phase transitions, where competing phases and enhanced fluctuations may give rise to unconventional phases.

cond-mat.mes-hall↗

arXiv2024

DrugAgent: Reliable Multi-Agent Integration of Conflicting Biomedical Evidence for Drug-Target Interaction Assessment

Workflows in drug-target interaction (DTI) assessment require integrating heterogeneous data from predictive models, curated resources, and observations from experimental literature. This evidence can be incomplete or conflicting. DrugAgent is a large language model (LLM)-based multi-agent system focused on DTI evidence integration that integrates outputs from machine learning, knowledge graph, and retrieval-augmented generation (RAG) agents. DrugAgent converts agent outputs into interpretable representations, then summarizes conflict across the evidence. We evaluated DrugAgent on kinase screening data of 900 pairs spanning 178 kinases and 42 inhibitors, and an androgen receptor antagonist screening benchmark. On the kinase dataset, LLM-as-a-Judge evaluation indicated outputs were faithful to input evidence in 98.8% of cases. Biological plausibility of returned summarization was high (scores 3-4 out of 5) across ground-truth classes: 79% of Weak activity labels cases (81% for Moderate/77% Strong); Strong cases received higher scores than Weak/Moderate. Label stability showed 98% agreement across runs. Results on the antagonist benchmark were consistent with the kinase dataset. Retrieved literature provided the greatest benefit when direct drug-target evidence was available, highlighting the importance of evidence availability for RAG-based integration. DrugAgent provides heterogeneous evidence-grounded DTI assessment, complementing standalone DTI prediction. We provide strategies to model agreement, conflict, and uncertainty in biomedical evidence integration. Code: https://github.com/sciluna/DrugAgent.

cs.AI↗