5 papers
An LLM agent for end-to-end computational materials discovery
Chen Yuntong, Huang Ju, Liu Yu +6
The coordination of multi-scale tasks is an effective strategy for computational materials discovery, yet the repeated application of diverse algorithms and tools renders it challe…
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…
Physically Verifiable Evidence and LLM-Based Reporting for Bearing Fault Diagnosis
Yuntong Chen, Jianyu Liu, Guobin Zhao +5
Trustworthy deployment of AI-based diagnosis in safety-critical mechanical systems hinges on validation: whether a prediction can be checked against physical reality before it is a…
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…