arXiv AI
LeMat-Synth: a multi-modal toolbox to curate broad synthesis procedure databases from scientific literature
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Large Language Models Transform Organic Synthesis From Reaction Prediction to Automation
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...
arXiv AI
Oct 26
RxnExBench: Benchmarking Vision-Language Models for Structured Chemical Reaction Extraction from Scientific Literature
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 AI
Aug 13
Chemically Meaningful Textualization Enables Explainable Validation of Metal-Organic Frameworks by Large Language Models
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 AI
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A Large-Scale Dataset for Molecular Structure-Language Description via a Rule-Regularized Method
arXiv:2602. 02320v4 Announce Type: replace-cross Abstract: Molecular function is largely determined by structure.
arXiv Machine Learning
Aug 6
DASyR-LLM: Domain-Aware Symbolic Regression with LLMs for Kinetic Model Discovery
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.
Hugging Face Trending Papers
Aug 5
DASyR-LLM: Domain-Aware Symbolic Regression with LLMs for Kinetic Model Discovery
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.