FrontierMat：面向材料研发前沿的语言模型基准测试

Authors:Feng Chen, Jianguo Huang, Yushan Xiao, Yue Li, Bijun Tang, Xuyu Dong, Xingguo Wang, Feiyang Xie, Jianhui Gu, Dongfeng Cao, Hongda Chen, Yongji Gong, Ruiqi Li, Shuxin Li, Xingji Li, Ruitao Lv, Li Song, Lei Wang, Ye Zhang, Xiaoxu Zhao, Xitao Zheng, Bo An, Zheng Liu

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Abstract:To accelerate materials discovery, language models must be able to address problems at the research frontier. Yet textbook- and examination-derived benchmarks primarily assess established knowledge, leaving performance on frontier research problems largely untested. We introduce FrontierMat, a benchmark of 330 problems drawn from frontier academic and industrial research in fields including aerospace, semiconductors, embodied intelligence and nuclear fusion. Every problem is validated by leading experimentalists and industry experts in its field. Each problem presents a real study and asks the model to infer a withheld outcome or draw a conclusion from selected experimental evidence. Problems are annotated by functional domain, material system and the scale of the governing mechanism; 130 require cross-scale reasoning. The strongest general-purpose model scores only 53.7%, and on matched problems, cross-scale problems score 8.7 points below single-scale ones. Half of the problems are solved by at most one model, and models fail chiefly by endorsing what is plausible rather than what was observed. A domain-adaptation study with meta-weighted continued pretraining and scale-modular self-distillation raises an open-weight model from 36.7% to 55.4%, matching the strongest proprietary model, with improvements on public materials benchmarks and no decline in aggregate performance on general benchmarks. FrontierMat provides a testbed for measuring progress toward language models that support frontier materials R&D.

Submission history

From: Feng Chen [view email]
[v1] Fri, 30 Oct 2026 02:37:21 UTC (536 KB)