15 citations · 15 across the 5 of their papers we have counts for
5 papers
An LLM agent for end-to-end computational materials discovery
Chen Yuntong, Huang Ju, Liu Yu +8
Coordinating multi-scale tasks is an effective strategy for computational materials discovery, yet the diverse algorithms and tools involved make this process highly challenging. H…
Chemically Meaningful Textualization Enables Explainable Validation of Metal-Organic Frameworks by Large Language Models
Guobin Zhao, Xiao-Yan Li
Computation-ready metal-organic framework (MOF) databases are essential for high-throughput screening, yet many reported crystal structures remain chemically unreasonable or disord…
A Multi-level Information Integration Framework for Physically Verifiable Fault Diagnosis of Rotating Machinery
Yuntong Chen, Jianyu Liu, Yingqi Li +6
Integrating multi-level information, from physical models through data-driven diagnostics to natural language reasoning, into verifiable decision chains is a growing need in intell…
MOF-Sleuth: Tool-Grounded Reward Alignment for Explainable Fine-Grained MOF CIF Auditing
Yu Liu, Zhiwei Yang, Diandian Guo +7
Large metal-organic framework (MOF) databases support simulation, screening, and machine learning through crystallographic information files (CIFs). Subtle chemical and structural…
MOFClassifier: A Machine Learning Approach for Validating Computation-Ready Metal-Organic Frameworks
Guobin Zhao, Pengyu Zhao, Yongchul G. Chung
The computational discovery and design of new crystalline materials, particularly metal-organic frameworks (MOFs), heavily relies on high-quality, computation-ready structural data…