arXiv:2607. 00924v1 Announce Type: new Abstract: Accelerating materials discovery requires AI systems that can generate scientifically valid hypotheses through multi-step, domain-grounded reasoning.
By Subhadeep Pal, Shashwat Sourav, Tirthankar Ghosal, Markus J. Buehler
arXiv:2601. 02424v2 Announce Type: replace-cross Abstract: The synthesis of nanocrystals has been highly dependent on trial-and-error, due to the complex correlation between synthesis parameters and physicochemical properties.
By Kai Gu, Yingping Liang, Senliang Peng, Aotian Guo, Haizheng Zhong, Ying Fu
arXiv:2510.26824v2 Announce Type: replace-cross
Abstract: Wide access to advanced experimental methods in materials science has given rise to an abundance of procedural knowledge, which is scattered...
By Magdalena Lederbauer, Siddharth Betala, Valerie Gentzke, Anamaria Leonescu, Amine Sehaba, Faris Flaifil, Ayush Jain, Alfonso Amayuelas, Nikhil Yelamarthy, Xiyao Li, Gr\'egoire Germain, Stefano Ribes, Stefan P. Schmid, Alexandre Nozadze, Anna Kelmanson, Sudheesh Kumar Ethirajan, Mohd Zaki, Elton Pan, Georgia Channing, Connor W. Coley, Philippe Schwaller, Roc\'io Mercado, Alexandre Duval, Mathilde L. D. Franckel, Samuel P. Gleason
arXiv:2606. 14215v1 Announce Type: new Abstract: The emergence of Large Language Models (LLMs) has inspired the vision of generating bespoke crystal materials directly from natural-language instructions, enabling users to design materials through intuitive, conversational interaction.
By Yusei Ito, Yuta Suzuki, Tomoya Murata, Masaki Adachi
arXiv:2509. 23071v2 Announce Type: replace-cross Abstract: Retrieval-augmented generation (RAG) agent development is hindered by the lack of executable ground-truth agent-environment interaction trajectories.
By Muzhi Li, Jinhu Qi, Yihong Wu, Minghao Zhao, Liheng Ma, Yifan Li, Xinyu Wang, Zhenghan Tai, Zixing Song, Yingxue Zhang, Ho-fung Leung, Irwin King
The paper introduces SALR, a schema‑anchored latent reasoning approach for generating logical forms in knowledge‑base question answering. SALR delays explicit schema commitments by generating continuous thoughts in hidden states and aligns these thoughts with a codebook of KB schema elements, guided by an alignment objective derived from gold logical forms. Experiments on GrailQA and WebQSP demonstrate that SALR consistently outperforms strong baselines, notably improving compositional question performance by 2.86 F1 points over TIARA.
By Guangze Gao, Zixuan Li, Sikui Zhang, Chunfeng Yuan, Wenjuan Li, Bing Li, Xiaolong Jin, Weiming Hu