arXiv AI By Wanyu Zhao, Wanbing Zhao

Exploring Structures in Physics Problems: Can AI Agents Discover Statistical Mechanical Mappings?

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arXiv:2607. 26367v1 Announce Type: new Abstract: An important skill in theoretical physics is to recognize when a new problem can be transformed into a known model.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv AI
Jun 6

AutoDFT: A Closed-Loop Multi-Agent Framework for Autonomous DFT Calculations

arXiv:2605. 26179v2 Announce Type: replace-cross Abstract: Density functional theory (DFT) serves as the basis for computational discovery in materials science and chemistry, yet each calculation demands extensive human effort: adjusting algorithms when convergence stalls, revising plans when unexpected physics emerges, and inserting steps as intermediate results reshape the problem.

By Penghui Yang, Zhonghan Zhang, Yue Li, Xinrun Wang, Yanchen Deng, Yuhao Lu, Bijun Tang, Zheng Liu, Bo An
arXiv AI
Sep 2

Agentic programs: an emerging form of scientific software in computational materials science

The article introduces the concept of agentic programs—scientific software that blends deterministic algorithms with bounded large‑language‑model (LLM) judgment, task‑specific verification, episodic maturation, and full delegation in production. It argues that recent LLM‑based agents enable this new form of computational materials science software. The authors illustrate the idea with DeMARS, an agentic program designed to build atomistic models from experimentally measured disordered crystal structures.

By Yunsung Lim, Haekwan Jeon, Jaesun Kim, Jisu Kim, Seungwu Han
arXiv AI
Aug 28

From Atomic to Agentic: Towards Interpretable Evaluation of LLMs' Agentic Mathematical Capabilities

The paper introduces a new benchmark that evaluates large language models (LLMs) on their agentic mathematical reasoning rather than just final answers. It aligns problem‑solving behaviors with a taxonomy of reusable mathematical atomic capabilities and includes planning, action, and feedback tasks in both textual and multimodal settings. Experiments show that models with similar end‑to‑end accuracy can have very different agentic profiles, highlighting the importance of process‑level evaluation.

By Jiayi Kuang, Yinghui Li, Yunze Song, Keyu Chen, Zhifeng Shen, Yangning Li, Yidong Wang, Di Yin, Ruizhi Qiao, Xing Sun, Kai Jin, Ying Shen, Liang Lin, Philip S. Yu
arXiv AI
Aug 14

PhysMaster: Building an Autonomous AI Physicist for Theoretical and Computational Physics Research

arXiv:2512. 19799v2 Announce Type: replace Abstract: Advances in LLM reasoning and tool use have enabled agentic science, yet frontier theoretical and computational physics remains challenging because research requires deep domain expertise, long-horizon reasoning, and reliable numerical computation.

By Tingjia Miao, Wenkai Jin, Jinxin Tan, Muhua Zhang, Xianghe Pang, Zexi Liu, Yuwen Du, Tian Jin, Tu Guo, Zhengliang Zhang, Jingkun Liu, Yuelin Hu, Jiejun Zhang, Yunjie Huang, Yuhan Wang, Wenbo Li, Yinuo Gao, Shuo Chen, Rui Ye, Yuzhi Zhang, Linfeng Zhang, Kun Chen, Wei Wang, Weinan E, Siheng Chen