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+@Article{Shi_arXiv_2025_p2503.06039,
+    author =   {Guoyong Shi and Fenglin Deng and Ri He and Dachuan Chen and Xuejiao
+             Chen and Peiheng Jiang and Zhicheng Zhong},
+    title =    {{Temperature-driven structural phase transitions in SmNiO{\$}{\_}3{\$}:
+             insights from deep potential molecular dynamics simulations}},
+    journal =  {arXiv},
+    year =     2025,
+    pages =    {2503.06039},
+    doi =      {10.48550/arXiv.2503.06039},
+    abstract = {The metal-insulator transition (MIT) in rare-earth nickelates
+             exemplifies the intricate interplay between electronic correlations
+             and lattice dynamics in quantum materials. This work focuses on
+             SmNiO{\$}{\_}3{\$} as a prototypical system, employing molecular
+             dynamics simulations based on a {''}hidden{''} magnetic potential
+             model. Our simulations reveal two key findings. First, the structural
+             phase transition in SmNiO{\$}{\_}3{\$} is intrinsically temperature-
+             driven and occurs spontaneously via collective lattice distortions.
+             Moreover, systematic high-pressure simulations demonstrate a distinct
+             pressure dependence of the transition temperature, which decreases
+             monotonically with increasing external hydrostatic pressure. These
+             results provide atomistic insights into the cooperative mechanisms
+             underlying the MIT and the interplay between structural distortions
+             and electron correlation effects. The computational approach developed
+             herein offers a generalizable framework for investigating complex
+             phase transitions in correlated quantum materials.},
+}
+
+@Article{Pei_arXiv_2025_p2406.05797,
+    author =   {Qizhi Pei and Rui Yan and Kaiyuan Gao and Jinhua Zhu and Lijun Wu},
+    title =    {{3D-MolT5: Leveraging Discrete Structural Information for Molecule-Text
+             Modeling}},
+    journal =  {arXiv},
+    year =     2025,
+    pages =    {2406.05797},
+    doi =      {10.48550/arXiv.2406.05797},
+    abstract = {The integration of molecular and natural language representations has
+             emerged as a focal point in molecular science, with recent
+             advancements in Language Models (LMs) demonstrating significant
+             potential for comprehensive modeling of both domains. However,
+             existing approaches face notable limitations, particularly in their
+             neglect of three-dimensional (3D) information, which is crucial for
+             understanding molecular structures and functions. While some efforts
+             have been made to incorporate 3D molecular information into LMs using
+             external structure encoding modules, significant difficulties remain,
+             such as insufficient interaction across modalities in pre-training and
+             challenges in modality alignment. To address the limitations, we
+             propose {\textbackslash}textbf{\{}3D-MolT5{\}}, a unified framework
+             designed to model molecule in both sequence and 3D structure spaces.
+             The key innovation of our approach lies in mapping fine-grained 3D
+             substructure representations into a specialized 3D token vocabulary.
+             This methodology facilitates the seamless integration of sequence and
+             structure representations in a tokenized format, enabling 3D-MolT5 to
+             encode molecular sequences, molecular structures, and text sequences
+             within a unified architecture. Leveraging this tokenized input
+             strategy, we build a foundation model that unifies the sequence and
+             structure data formats. We then conduct joint pre-training with multi-
+             task objectives to enhance the model's comprehension of these diverse
+             modalities within a shared representation space. Thus, our approach
+             significantly improves cross-modal interaction and alignment,
+             addressing key challenges in previous work. Further instruction tuning
+             demonstrated that our 3D-MolT5 has strong generalization ability and
+             surpasses existing methods with superior performance in multiple
+             downstream tasks. Our code is available at
+             https://github.com/QizhiPei/3D-MolT5.},
+}
+
 @Article{Chen_arXiv_2025_p2506.00880,
     author =   {Zhuo Chen and Yizhen Zheng and Huan Yee Koh and Hongxin Xiang and
              Linjiang Chen and Wenjie Du and Yang Wang},