Materials Scholar Website
This is the repo that contains the code for the Materials Scholar website, which is built with Plotly Dash. Matscholar is a research effort based at Lawrence Berkeley National Laboratory with the aim of organize the world's materials knowleged through the application of Natural Language Processing (NLP) to materials science literature. To date, we have extracted and curated useful materials data from over 5 million materials abstracts and it is freely accessible via our website and API.
If you would like to submit feedback to help us improve the app, please create a new issue on this repository. If you have any other questions you can reach the Matscholar team at help@matscholar.com.
Matscholar is supported by Toyota Research Institute through the Accelerated Materials Design and Discovery program. Abstract data was downloaded from the ScienceDirect API between October 2017 and September 2018, and the Scopus API in July 2019 via http://api.elsevier.com and http://www.scopus.com. Abstract data was also downloaded from the SpringerNature API and Royal Society of Chemistry sources.
References
[1] Weston, L., Tshitoyan, V., Dagdelen, J., Kononova, O., Trewartha, A., Persson, K., Ceder, G., Jain, A. (2019) Named Entity Recognition and Normalization Applied to Large-Scale Information Extraction from the Materials Science Literature. J. Chem. Inf. Model.
[2] Tshitoyan, V., Dagdelen, J., Weston, L., Dunn, A., Rong, Z., Kononova, O., … Jain, A. (2019). Unsupervised word embeddings capture latent knowledge from materials science literature. Nature, 571(7763), 95–98. https://doi.org/10.1038/s41586-019-1335-8
Contributors
@jdagdelen, @lweston, @AmalieT, @vtshitoyan, @computron