| About this Abstract |
| Meeting |
MS&T22: Materials Science & Technology
|
| Symposium
|
Ceramics and Glasses Modeling by Simulations and Machine Learning
|
| Presentation Title |
Natural Language Processing Aided Understanding of Material Science Literature |
| Author(s) |
Mohd Zaki, Tanishq Gupta, N. M. Anoop Krishnan, Mausam Mausam |
| On-Site Speaker (Planned) |
N. M. Anoop Krishnan |
| Abstract Scope |
Material science literature has been an indispensable and reliable source of information for designing materials for targeted applications. Many research papers are now available, which can be referred by researchers to come up with novel materials for answering industrial and societal needs. However, it is humanly impossible to go through and understand all the published research literature. In this work, we use a natural language processing based solution by training a language model, namely MatSciBERT, on materials science literature. The model’s capability to understand the material science domain by evaluating its performance on downstream tasks of named entity recognition, abstract classification, and relation classification is evident in the achieved state of the art results on these tasks. We have made all the resources publicly available for the scientific community to use and accelerate material discovery. |