Title: Unsupervised word embeddings capture latent knowledge from materials science literature

Abstract

The overwhelming majority of scientific knowledge is published as text, which is difficult to analyse by either traditional statistical analysis or modern machine learning methods. By contrast, the main source of machine-interpretable data for the materials research community has come from structured property databases, which encompass only a small fraction of the knowledge present in the research literature. Beyond property values, publications contain valuable knowledge regarding the connections and relationships between data items as interpreted by the authors. To improve the identification and use of this knowledge, several studies have focused on the retrieval of information from scientific literature using supervised natural language processing, which requires large hand-labelled datasets for training. Here we show that materials science knowledge present in the published literature can be efficiently encoded as information-dense word embeddings (vector representations of words) without human labelling or supervision. Without any explicit insertion of chemical knowledge, these embeddings capture complex materials science concepts such as the underlying structure of the periodic table and structure-property relationships in materials. Furthermore, we demonstrate that an unsupervised method can recommend materials for functional applications several years before their discovery. This suggests that latent knowledge regarding future discoveries is to a large extent embeddedmore » in past publications. Lastly, our findings highlight the possibility of extracting knowledge and relationships from the massive body of scientific literature in a collective manner, and point towards a generalized approach to the mining of scientific literature.« less

Authors:

Tshitoyan, Vahe ^[1]; Dagdelen, John ^[2]; Weston, Leigh ^[3]; Dunn, Alexander ^[2]; Rong, Ziqin ^[3]; Kononova, Olga ^[4]; Persson, Kristin A. ^[2]; Ceder, Gerbrand ^[2]; Jain, Anubhav ^[3]

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+ Show Author Affiliations

1. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Google LLC, Mountain View, CA (United States)
2. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Univ. of California, Berkeley, CA (United States)
3. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
4. Univ. of California, Berkeley, CA (United States)

Publication Date:

Wed Jul 03 00:00:00 EDT 2019

Research Org.:

Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)

Sponsoring Org.:

USDOE Office of Science (SC), Basic Energy Sciences (BES)

OSTI Identifier:

1608271

Grant/Contract Number:  

AC02-05CH11231

Resource Type:

Accepted Manuscript

Journal Name:

Nature (London)

Additional Journal Information:

Journal Name: Nature (London); Journal Volume: 571; Journal Issue: 7763; Journal ID: ISSN 0028-0836

Publisher:

Nature Publishing Group

Country of Publication:

United States

Language:

English

Subject:

36 MATERIALS SCIENCE