Matscholar is a research effort based at Lawrence Berkeley National Laboratory with the aim of organizing the world's materials knowlegde by applying 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 public API.
This repo houses code for an early version of the matscholar website, which which was built with Plotly Dash. Please note that the live website is no longer using this codebase.
If you have any questions please contact our development team at [email protected].
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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.
If you find Matscholar is useful for your research, consider citing our efforts:
[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. link
[2] Tshitoyan, V., Dagdelen, J., Weston, L., Dunn, A., Rong, Z., Kononova, O., Persson, K., Ceder, G., Jain, A. (2019). Unsupervised word embeddings capture latent knowledge from materials science literature. Nature, 571(7763), 95–98. link
@jdagdelen, @lweston, @AmalieT, @vtshitoyan, @computron, @ardunn