An overwhelmingly large amount of knowledge in the materials domain is
generated and stored as text published in peer-reviewed scientific literature.
Recent developments in natural language processing, such as bidirectional
encoder representations from transformers (BERT) models, provide promising
tools to extract information from these texts. However, direct application of
these models in the materials domain may yield suboptimal results as the models
themselves may not be trained on notations and jargon that are specific to the
domain. Here, we present a materials-aware language model, namely, MatSciBERT,
which is trained on a large corpus of scientific literature published in the
materials domain. We further evaluate the performance of MatSciBERT on three
downstream tasks, namely, abstract classification, named entity recognition,
and relation extraction, on different materials datasets. We show that
MatSciBERT outperforms SciBERT, a language model trained on science corpus, on
all the tasks. Further, we discuss some of the applications of MatSciBERT in
the materials domain for extracting information, which can, in turn, contribute
to materials discovery or optimization. Finally, to make the work accessible to
the larger materials community, we make the pretrained and finetuned weights
and the models of MatSciBERT freely accessible.

本文介绍了在材料科学文献数据的基础上训练的一种面向材料科学领域的语言模型 MatSciBERT，并对其在抽象分类、命名实体识别和关系提取等领域的性能进行了评价。结果表明 MatSciBERT 在这些任务中具有优异的表现，可用于提取材料领域的信息以推动材料的发现或优化。同时我们提供 MatSciBERT 的预训练和微调权重，以及模型的免费访问权。