2025/12/19 by Yanzhen Wang, Yiyang Jiang, Wang, Yanzhen +23
Materials Science · Physics and Astronomy · #Artificial Intelligence (cs.AI) #Electronic and Structural Properties of Oxides #FOS: Computer and information sciences #FOS: Physical sciences #Machine Learning in Materials Science #Materials Science (cond-mat.mtrl-sci) #Quantum many-body systems
paper · doi:10.48550/arxiv.2512.19753
openalex publication_date 2025/12/19 · openalex created_date 2025/12/25 · openalex updated_date 2026/07/28
We introduce QMBench, a comprehensive benchmark designed to evaluate the capability of large language model agents in quantum materials research. This specialized benchmark assesses the model's ability to apply condensed matter physics knowledge and computational techniques such as density functional theory to solve research problems in quantum materials science. QMBench encompasses different domains of the quantum material research, including structural properties, electronic properties, thermodynamic and other properties, symmetry principle and computational methodologies. By providing a standardized evaluation framework, QMBench aims to accelerate the development of an AI scientist capable of making creative contributions to quantum materials research. We expect QMBench to be developed and constantly improved by the research community.