Harnessing AtomisticSkills for Agentic Atomistic Research
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arXiv
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| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866911710295621632 |
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| author | Deng, Bowen Li, Bohan Cox, Matthew Chun, Hoje Nam, Juno Lyssenko, Artur Edamadaka, Sathya Ruza, Jurgis Du, Xiaochen Segal, Nofit Sanchez, Jesus Diaz Xie, Mingrou Perez, Ty Yao, Yu Steiner, Miguel Majumdar, Sauradeep Musgrave III, Charles B. Chandra, Anirban Patra, Abhirup Hohl, Detlef Coley, Connor W. Li, Ju Gómez-Bombarelli, Rafael |
| author_facet | Deng, Bowen Li, Bohan Cox, Matthew Chun, Hoje Nam, Juno Lyssenko, Artur Edamadaka, Sathya Ruza, Jurgis Du, Xiaochen Segal, Nofit Sanchez, Jesus Diaz Xie, Mingrou Perez, Ty Yao, Yu Steiner, Miguel Majumdar, Sauradeep Musgrave III, Charles B. Chandra, Anirban Patra, Abhirup Hohl, Detlef Coley, Connor W. Li, Ju Gómez-Bombarelli, Rafael |
| contents | Computational materials science and chemistry span vast knowledge domains and fractured software ecosystems. Although large language models (LLMs) have demonstrated research capabilities, scaling monolithic agents to manage the rigor and complexity of atomistic research remains a challenge. Here, we introduce AtomisticSkills, an open-source harness framework that empowers general-purpose AI coding agents to conduct atomistic research across materials science, chemistry, and drug discovery. By hierarchically decomposing scientific workflows into agent skills and tools, AtomisticSkills provides agents with modular, extensible, and plug-and-play research capabilities. The framework integrates more than 100 human-curated multidisciplinary skills, including database access, thermodynamics and kinetics modeling, and diverse simulation engines employing machine learning interatomic potentials (MLIPs) and density functional theory (DFT). We validate its functional coverage against scientific literature and demonstrate robust orchestration capabilities across diverse scientific campaigns: generative design of Li-ion solid-state electrolytes, high-throughput screening of metal-organic frameworks for CO2 capture, autonomous MLIP benchmarking and fine-tuning, multi-stage structure-based virtual screening for drug design, multimodal X-ray diffraction pattern analysis, and screening of Fe-oxide catalysts for oxygen evolution reaction. AtomisticSkills provides a critical agent infrastructure towards building fully autonomous AI scientists. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_24002 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Harnessing AtomisticSkills for Agentic Atomistic Research Deng, Bowen Li, Bohan Cox, Matthew Chun, Hoje Nam, Juno Lyssenko, Artur Edamadaka, Sathya Ruza, Jurgis Du, Xiaochen Segal, Nofit Sanchez, Jesus Diaz Xie, Mingrou Perez, Ty Yao, Yu Steiner, Miguel Majumdar, Sauradeep Musgrave III, Charles B. Chandra, Anirban Patra, Abhirup Hohl, Detlef Coley, Connor W. Li, Ju Gómez-Bombarelli, Rafael Chemical Physics Materials Science Artificial Intelligence Computational Physics Computational materials science and chemistry span vast knowledge domains and fractured software ecosystems. Although large language models (LLMs) have demonstrated research capabilities, scaling monolithic agents to manage the rigor and complexity of atomistic research remains a challenge. Here, we introduce AtomisticSkills, an open-source harness framework that empowers general-purpose AI coding agents to conduct atomistic research across materials science, chemistry, and drug discovery. By hierarchically decomposing scientific workflows into agent skills and tools, AtomisticSkills provides agents with modular, extensible, and plug-and-play research capabilities. The framework integrates more than 100 human-curated multidisciplinary skills, including database access, thermodynamics and kinetics modeling, and diverse simulation engines employing machine learning interatomic potentials (MLIPs) and density functional theory (DFT). We validate its functional coverage against scientific literature and demonstrate robust orchestration capabilities across diverse scientific campaigns: generative design of Li-ion solid-state electrolytes, high-throughput screening of metal-organic frameworks for CO2 capture, autonomous MLIP benchmarking and fine-tuning, multi-stage structure-based virtual screening for drug design, multimodal X-ray diffraction pattern analysis, and screening of Fe-oxide catalysts for oxygen evolution reaction. AtomisticSkills provides a critical agent infrastructure towards building fully autonomous AI scientists. |
| title | Harnessing AtomisticSkills for Agentic Atomistic Research |
| topic | Chemical Physics Materials Science Artificial Intelligence Computational Physics |
| url | https://arxiv.org/abs/2605.24002 |