arXiv:2607. 19406v1 Announce Type: new Abstract: Structural elucidation from Nuclear Magnetic Resonance (NMR) data remains a fundamental bottleneck across chemistry, materials science, and biology.
arXiv:2603. 23101v3 Announce Type: replace Abstract: Intelligent spectroscopy serves as a pivotal element in AI-driven closed-loop scientific discovery, functioning as the critical bridge between matter structure and artificial intelligence.
arXiv:2608.30910v1 Announce Type: new Abstract: Spectroscopic structure elucidation is central to molecular analysis, but recent Large Language Model (LLM)-based methods mostly formulate it as direct...
arXiv:2512. 18531v2 Announce Type: replace-cross Abstract: One-dimensional NMR spectroscopy is one of the most widely used techniques for the characterization of organic compounds and natural products.
arXiv:2608. 14720v1 Announce Type: cross Abstract: Following the molecular discovery and synthesis revolutions, scalable automated structure elucidation from routine spectroscopic data remains an outstanding challenge.
The paper introduces a semi‑supervised framework that learns to predict nuclear magnetic resonance (NMR) chemical shifts from millions of literature‑extracted spectra without explicit atom‑level assignments. By treating the prediction as a permutation‑invariant set supervision problem, the authors show that optimal bipartite matching can be reduced to a sorting‑based loss, enabling stable large‑scale training. The resulting models outperform state‑of‑the‑art methods, generalize better to diverse molecules, and for the first time capture systematic solvent effects across common NMR solvents.
arXiv:2606. 30961v1 Announce Type: cross Abstract: Advances in deep learning architectures and representations have enabled ML-driven chemical property prediction, but state-of-the-art (SOTA) models have remained largely confined to independent codebases and lack support for diverse chemical species.
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. 19816v1 Announce Type: cross Abstract: Determining molecular structures from spectroscopic data remains fundamentally challenging because the inverse problem is intrinsically underdetermined: individual spectra are sparse, low-dimensional, and encode only partial structural evidence relative to the vast space of possible molecules.
El Agente Potente is an agentic system that integrates typed execution graphs and a coding mode to facilitate machine‑learning interatomic potential (MLIP) driven atomistic simulations. Typed execution graphs offer structured, provenance‑aware workflows where large language models handle planning and routing while deterministic Python code performs scientific computation and validation. The coding agent builds customized workflows for tasks needing procedural flexibility, invoking existing Potente functions for supported calculations. The system is demonstrated across materials discovery, energy‑landscape exploration, adsorption, and catalytic reaction workflows, with benchmarks on reproducibility and LLM token cost.
arXiv:2512. 19733v3 Announce Type: replace-cross Abstract: Molecular structure elucidation from spectroscopic data is a long-standing challenge in Chemistry, traditionally requiring expert interpretation.
arXiv:2606. 29776v1 Announce Type: cross Abstract: 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.
The paper introduces Align-React, a chemical reaction representation learning framework that incorporates atomic correspondence between reactants and products, an adapter for embedding reaction conditions, and a Reaction-Center-Aware attention mechanism. These components enable the model to capture precise molecular transformations and focus on critical functional groups, leading to improved performance across a variety of organic reaction tasks. The framework outperforms existing architectures on most benchmark datasets.