Data preparation, including source profiling, quality diagnosis, and cleaning, remains a labor-intensive bottleneck in data-driven applications. Traditional approaches require manual rule design for each data source and lack adaptability to heterogeneous formats. This paper proposes an LLM-driven intelligent agent for...
The proposed Diagnostic Evidence Network (DENet) is an encoder-agnostic multi-task framework that extends the output to a structured evidence record: the classification, a predicted characteristic frequency comparable against the theoretical value determined by bearing geometry and shaft speed, and a temporal localizat...
Yun-Tong Chen, Jian-Yu Liu, Ying-Qi Li et al.· 1 citation
Computation-ready metal-organic framework (MOF) databases are essential for high-throughput screening, yet many reported crystal structures remain chemically unreasonable or disordered, compromising simulation fidelity. Existing validation approaches can identify non-computation-ready structures, but they often rely on...
By connecting the heterogeneous stages of computational materials discovery, the LLM-based agents of MAESTRO can operate across application domains and uncover high-performance materials that conventional screening approaches would be unlikely to consider.
Yun-Tong Chen, Ju Huang, Yu Liu et al.· 0 citations
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