Unsupervised word embeddings capture latent knowledge from materials science literature
Open accessAbstract
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 1,2, 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 3-10, 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 11-13 (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 embedded in past publications. 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.
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NLP extraction of process-structure-property-performance relationships by mining text corpora has enabled discovery of new materials and identification of materials synthesis procedures.
“Another exciting outcome from NLP is to extract the process-structure-property-performance relationships by mining text corpora, which has enabled one to discover new materials 145 and identify materials synthesis procedures 146 , although there have been limited applications of this approach in concrete science.”
From Machine learning in concrete science: applications, challenges, and best practices (2022) · cites this paper as [145]
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- Authors
- Tshitoyan, V., Dagdelen, J., Weston, L., Dunn, A., Rong, Z., Kononova, O., Persson, K. A., Ceder, G., & Jain, A.
- Affiliations
- Google (United States) (US)
- Lawrence Berkeley National Laboratory (US)
- University of California, Berkeley (US)
- Corresponding author
- Tshitoyan, Vahe
- Ceder, Gerbrand
- Jain, Anubhav
- ORCID
- Venue
- Nature
- Published
- 2019-07-03
- Volume
- 571
- Issue
- 7763
- Pages
- 95-98
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- OpenAlex
- W2953641512
- Cited by
- 1,123
- References
- 41
- Citations by year
- 2026: 157 · 2025: 159 · 2024: 165 · 2023: 146 · 2022: 162 · 2021: 148 · 2020: 148 · 2019: 38
- FWCI
- 44.77
- Type
- journal article
- Language
- en
- Open access
- green
- escholarship.org
- Licence
- Locations
- Nature (publishedVersion)
- PubMed (publishedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Funders
- Toyota Research Institute
- Topics
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Social Sciences › General Social Sciences › Computational and Text Analysis Methods
- Keywords
- word embeddings, materials science literature, scientific literature mining, structure-property relationships, periodic table structure, materials database
- MeSH terms
- Unsupervised Machine Learning, Materials Science, Electric Conductivity, Electrodes, Iron, Lithium, Magnetics, Natural Language Processing, Terminology as Topic, Research, Semantics, Temperature, Reproducibility of Results, Knowledge, Data Mining, Research Report
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