arXiv:2508.05427v2 Announce Type: replace Abstract: Large language models (LLMs) are beginning to reshape how organic-synthesis workflows are represented, queried, planned, and connected to experimen...
RxnExBench is a multimodal benchmark designed to evaluate vision‑language models on the task of extracting structured chemical reaction data from dense figures in synthetic chemistry literature. The dataset, compiled from high‑impact journals, contains 3,565 expert‑annotated reactions and 2,361 molecular structures, organized under a unified schema of 15 reaction roles and 26 core fields. It also introduces a difficulty taxonomy, a modular annotation pipeline, and the Oracle‑MolRec protocol to separate molecular recognition from higher‑level reasoning, and shows that current state‑of‑the‑art models struggle as schema complexity and reasoning demands increase.
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.
arXiv:2602. 02320v4 Announce Type: replace-cross Abstract: Molecular function is largely determined by structure.
arXiv:2608. 05120v1 Announce Type: new Abstract: Kinetic model discovery is a central challenge in chemical engineering, as accurate rate expressions are essential for understanding and controlling chemical and biological processes.
Kinetic model discovery is a central challenge in chemical engineering, as accurate rate expressions are essential for understanding and controlling chemical and biological processes. Symbolic regression (SR) has emerged as a powerful data-driven approach for identifying interpretable kinetic models, but usually operates without domain knowledge, often exploring physicochemically implausible models.
arXiv:2608. 07454v1 Announce Type: cross Abstract: The total synthesis of a complex molecule is among the most demanding intellectual and experimental feats in chemistry: a chemist must plan many steps ahead for how to assemble simple building blocks into an intricate target, devise backup strategies, and anticipate procedural challenges.
arXiv:2606. 05693v1 Announce Type: new Abstract: Large language models (LLMs) have shown promise for molecular property prediction, but their ability to reason over chemical structures remains limited, as molecular representations such as SMILES differ substantially from the natural language on which LLMs are primarily trained.
The paper introduces SciKGExtract, a schema-guided framework that uses large‑language‑model extraction, chemical normalization, and agent‑based evaluation to convert heterogeneous materials science literature into a knowledge graph. Applied to 176 atomic‑layer‑deposition papers on ZnO and IGZO, the system improves extraction F1 from 0.591 to 0.805 for ZnO with agentic refinement, while IGZO remains more challenging at 0.344. Evaluation against a detailed schema of 65 experimental properties and 155 quantitative nodes reveals segmentation and numerical assignment errors, highlighting the complementary role of chemical canonicalization and agentic verification in producing machine‑actionable experimental knowledge.
arXiv:2607. 07708v1 Announce Type: cross Abstract: Structure-property relationships are foundational to biology, chemistry and materials science, where function, reactivity and physical response emerge from spatial, chemical and periodic organization.
arXiv:2606. 13477v1 Announce Type: cross Abstract: Supramolecular chemistry, which includes the study of non-covalent host-guest assemblies, has advanced various applications.
ChemVTS-Bench is a domain-authentic benchmark that evaluates Visual‑Textual‑Symbolic reasoning in multimodal large language models for chemistry. It presents diverse chemical problems—organic molecules, inorganic materials, and 3D crystal structures—in three input modes: visual-only, visual‑text hybrid, and SMILES-based symbolic. The benchmark includes an automated agent workflow for inference, answer verification, and failure diagnosis, and shows that visual-only inputs and structural chemistry remain challenging for current models.