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:2608. 11283v1 Announce Type: cross Abstract: Computation-ready metal-organic framework (MOF) databases are essential for high-throughput screening, yet many reported crystal structures remain chemically unreasonable or disordered, compromising simulation fidelity.
By Guobin Zhao, Xiao-Yan Li
MAESTRO is a large language model agent that automates the full screening pipeline for metal‑organic frameworks (MOFs). It parses extensive MOF literature, links publications to crystal structures, curates a computation‑ready database, and then applies a progressively more expensive computational strategy to identify promising candidates. The identified materials for wet flue gas separation come from unrelated studies, demonstrating the agent’s ability to uncover high‑performance materials across domains.
By Chen Yuntong, Huang Ju, Liu Yu, Zhao Dan, Sun Mingqi, Ju Chentian, Liu Yanbing, Huang Lijiang, Zhao Guobin
arXiv:2606. 03660v1 Announce Type: new Abstract: Large language models are increasingly used as chemistry assistants, yet most chemistry benchmarks still score only final answers.
By Hongyu Guo, Hao Li, He Cao, Gongbo Zhang, Li Yuan
Nuclear Magnetic Resonance (NMR) spectroscopy is the gold standard for molecular structure elucidation, yet interpreting complex spectra for unknown molecules remains a bottleneck reliant on human expertise. While artificial intelligence has advanced this field, current methods face a critical trade-off: database retrieval cannot identify novel scaffolds, while de novo molecular structure elucidation models operate as black boxes, lacking the atom-level interpretability required for rigorous scientific validation.
arXiv:2607. 19935v1 Announce Type: new Abstract: Large metal-organic framework (MOF) databases support simulation, screening, and machine learning through crystallographic information files (CIFs).
By Yu Liu, Zhiwei Yang, Diandian Guo, Kun Peng, Fangfang Yuan, Cong Cao, Chaozhuo Li, Zhiyuan Ma, Yanbing Liu, Guobin Zhao