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| Autores principales: | , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2604.06076 |
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| _version_ | 1866917388518162432 |
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| author | Zeng, Lianduan Zhou, Xiao Zheng, Xueru Gao, Ning Liu, Lei Cao, Yunxuan Chen, Hongjian Wang, Zhongyang Fan, Tongxiang |
| author_facet | Zeng, Lianduan Zhou, Xiao Zheng, Xueru Gao, Ning Liu, Lei Cao, Yunxuan Chen, Hongjian Wang, Zhongyang Fan, Tongxiang |
| contents | With the advancement of the Materials Genome Initiative, high-throughput computation has become central to accelerating materials discovery. However, conventional first-principles workflows are cumbersome and error-prone. Existing high-throughput tools, while efficient at batch job submission, lack intelligence: they cannot automatically plan tasks based on scientific objectives or dynamically adapt workflows according to intermediate results. To address these limitations, this paper proposes and implements HTC-Claw, an intelligent high-throughput computational platform built upon the OpenClaw framework. The key innovations of HTC-Claw are: 1) An agent-based framework for automatic decomposition of high-level research goals into parallelizable task sets; 2) A closed-loop execution engine that integrates real-time analysis and reporting; 3) Adaptive decision-making and workflow iteration capabilities based on intermediate results; and 4) A decoupled, modular architecture that separates the scheduling system from functional modules, enhancing extensibility and robustness. Case studies demonstrate that HTC-Claw enables an intelligent, end-to-end workflow from user intent to final reporting in materials exploration |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_06076 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | The HTC-Claw: Automating Discovery through High-Throughput Computational Campaigns Zeng, Lianduan Zhou, Xiao Zheng, Xueru Gao, Ning Liu, Lei Cao, Yunxuan Chen, Hongjian Wang, Zhongyang Fan, Tongxiang Materials Science With the advancement of the Materials Genome Initiative, high-throughput computation has become central to accelerating materials discovery. However, conventional first-principles workflows are cumbersome and error-prone. Existing high-throughput tools, while efficient at batch job submission, lack intelligence: they cannot automatically plan tasks based on scientific objectives or dynamically adapt workflows according to intermediate results. To address these limitations, this paper proposes and implements HTC-Claw, an intelligent high-throughput computational platform built upon the OpenClaw framework. The key innovations of HTC-Claw are: 1) An agent-based framework for automatic decomposition of high-level research goals into parallelizable task sets; 2) A closed-loop execution engine that integrates real-time analysis and reporting; 3) Adaptive decision-making and workflow iteration capabilities based on intermediate results; and 4) A decoupled, modular architecture that separates the scheduling system from functional modules, enhancing extensibility and robustness. Case studies demonstrate that HTC-Claw enables an intelligent, end-to-end workflow from user intent to final reporting in materials exploration |
| title | The HTC-Claw: Automating Discovery through High-Throughput Computational Campaigns |
| topic | Materials Science |
| url | https://arxiv.org/abs/2604.06076 |