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.
@article{arxiv.2512.19753,
title = {QMBench: A Research Level Benchmark for Quantum Materials Research},
author = {Yanzhen Wang and Yiyang Jiang and Diana Golovanova and Kamal Das and Hyeonhu Bae and Yufei Zhao and Huu-Thong Le and Abhinava Chatterjee and Yunzhe Liu and Chao-Xing Liu and Felipe H. da Jornada and Binghai Yan and Xiao-Liang Qi},
journal= {arXiv preprint arXiv:2512.19753},
year = {2025}
}