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Multi-source AI news clustered, deduplicated, and scored 0–100 across authority, cluster strength, headline signal, and time decay.

  1. TOOL · arXiv cs.AI English(EN) ·

    Harnessing AtomisticSkills for Agentic Atomistic Research

    Researchers have developed AtomisticSkills, an open-source framework designed to enable AI coding agents to perform complex atomistic research across materials science, chemistry, and drug discovery. This framework organizes scientific workflows into modular skills and tools, integrating over 100 curated capabilities such as database access, thermodynamic modeling, and simulation engines. AtomisticSkills has been validated through diverse scientific campaigns, including the generative design of electrolytes and catalyst screening, positioning it as crucial infrastructure for developing autonomous AI scientists. AI

    IMPACT Enables AI agents to autonomously conduct complex scientific research, potentially accelerating discovery in materials science and chemistry.

  2. RESEARCH · arXiv cs.LG English(EN) · · [2 sources]

    Atom-anchored LLMs speak Chemistry: A Retrosynthesis Demonstration

    Researchers have developed new benchmarks and methods to evaluate and enhance Large Language Models (LLMs) for chemistry-related tasks. One approach, Speak-to-Structure (S^2-Bench), focuses on open-domain molecule generation, moving beyond simple one-to-one mappings to assess creative and diverse molecular design capabilities. Another method introduces atom-anchored LLMs that use unique atomic identifiers to anchor chain-of-thought reasoning for molecular transformations, achieving high success rates in tasks like retrosynthesis without requiring task-specific training. AI

    IMPACT New benchmarks and methods are emerging to push LLMs towards more complex scientific reasoning in chemistry.