QMBench: A Research Level Benchmark for Quantum Materials Research
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arXiv
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| Hauptverfasser: | , , , , , , , , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| author | Wang, Yanzhen Jiang, Yiyang Golovanova, Diana Das, Kamal Bae, Hyeonhu Zhao, Yufei Le, Huu-Thong Chatterjee, Abhinava Liu, Yunzhe Liu, Chao-Xing da Jornada, Felipe H. Yan, Binghai Qi, Xiao-Liang |
| author_facet | Wang, Yanzhen Jiang, Yiyang Golovanova, Diana Das, Kamal Bae, Hyeonhu Zhao, Yufei Le, Huu-Thong Chatterjee, Abhinava Liu, Yunzhe Liu, Chao-Xing da Jornada, Felipe H. Yan, Binghai Qi, Xiao-Liang |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_19753 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | QMBench: A Research Level Benchmark for Quantum Materials Research Wang, Yanzhen Jiang, Yiyang Golovanova, Diana Das, Kamal Bae, Hyeonhu Zhao, Yufei Le, Huu-Thong Chatterjee, Abhinava Liu, Yunzhe Liu, Chao-Xing da Jornada, Felipe H. Yan, Binghai Qi, Xiao-Liang Materials Science Artificial Intelligence 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. |
| title | QMBench: A Research Level Benchmark for Quantum Materials Research |
| topic | Materials Science Artificial Intelligence |
| url | https://arxiv.org/abs/2512.19753 |