Data-driven materials research enabled by natural language processing and information extraction

EA Olivetti, JM Cole, E Kim, O Kononova… - Applied Physics …, 2020 - pubs.aip.org
Given the emergence of data science and machine learning throughout all aspects of
society, but particularly in the scientific domain, there is increased importance placed on …

Opportunities and challenges for machine learning in materials science

D Morgan, R Jacobs - Annual Review of Materials Research, 2020 - annualreviews.org
Advances in machine learning have impacted myriad areas of materials science, such as
the discovery of novel materials and the improvement of molecular simulations, with likely …

Quantifying the advantage of domain-specific pre-training on named entity recognition tasks in materials science

A Trewartha, N Walker, H Huo, S Lee, K Cruse… - Patterns, 2022 - cell.com
A bottleneck in efficiently connecting new materials discoveries to established literature has
arisen due to an increase in publications. This problem may be addressed by using named …

MatSciBERT: A materials domain language model for text mining and information extraction

T Gupta, M Zaki, NMA Krishnan, Mausam - npj Computational Materials, 2022 - nature.com
A large amount of materials science knowledge is generated and stored as text published in
peer-reviewed scientific literature. While recent developments in natural language …

An analysis of simple data augmentation for named entity recognition

X Dai, H Adel - ar** pace with
rapid literature advances is increasingly time intensive. Automated synthesis protocol …