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
@article{arxiv.2604.06076,
title = {The HTC-Claw: Automating Discovery through High-Throughput Computational Campaigns},
author = {Lianduan Zeng and Xiao Zhou and Xueru Zheng and Ning Gao and Lei Liu and Yunxuan Cao and Hongjian Chen and Zhongyang Wang and Tongxiang Fan},
journal= {arXiv preprint arXiv:2604.06076},
year = {2026}
}