Opportunities and challenges of text mining in materials research
Open accessAbstract
Research publications are the major repository of scientific knowledge. However, their unstructured and highly heterogenous format creates a significant obstacle to large-scale analysis of the information contained within. Recent progress in natural language processing (NLP) has provided a variety of tools for high-quality information extraction from unstructured text. These tools are primarily trained on non-technical text and struggle to produce accurate results when applied to scientific text, involving specific technical terminology. During the last years, significant efforts in information retrieval have been made for biomedical and biochemical publications. For materials science, text mining (TM) methodology is still at the dawn of its development. In this review, we survey the recent progress in creating and applying TM and NLP approaches to materials science field. This review is directed at the broad class of researchers aiming to learn the fundamentals of TM as applied to the materials science publications.
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In experimental data sets extracted from the literature, there can be positive bias for reporting only successful experiments, leading to challenges in classification tasks.
“For example, in experimental data sets extracted from the literature (14,15,19), there can be positive bias for reporting only successful experiments (16, 107, 108), leading to challenges in classification tasks (17).”
From Machine Learning for the Discovery, Design, and Engineering of Materials. (2022) · cites this paper as [15]
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Recent advances and applications of deep learning methods in materials science
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Machine Learning for the Discovery, Design, and Engineering of Materials.
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- Authors
- Kononova, O., He, T., Huo, H., Trewartha, A., Olivetti, E. A., & Ceder, G.
- Affiliations
- Lawrence Berkeley National Laboratory (US)
- Massachusetts Institute of Technology (US)
- Corresponding author
- Ceder, Gerbrand
- Venue
- iScience
- Publisher
- Elsevier BV
- Published
- 2021-03
- Volume
- 24
- Issue
- 3
- Pages
- 102155
- ISSN
- 2589-0042 (ISSN-L)
- PMC
- PMC7905448
- OpenAlex
- W3127365350
- Cited by
- 182
- References
- 149
- Citations by year
- 2026: 34 · 2025: 30 · 2024: 40 · 2023: 37 · 2022: 31 · 2021: 10
- FWCI
- 1.81
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- iScience (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- eScholarship (California Digital Library) (submittedVersion, other-oa)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc-nd)
- eScholarship (California Digital Library) (submittedVersion)
- OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
- Funders
- National Science Foundation (1922090, 1922311, 1922372)
- Division of Materials Sciences and Engineering (DE-AC02-05-CH11231, KCD2S2)
- Office of Naval Research (00014-16-1-2432)
- Energy Biosciences Institute
- U.S. Department of Energy
- US Department of Energy Office of Science
- Basic Energy Sciences
- Topics
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Biomedical Text Mining and Ontologies
- Social Sciences › General Social Sciences › Computational and Text Analysis Methods
- Keywords
- text mining, natural language processing, information extraction, scientific texts, information retrieval, terminology, unstructured text, scientific publications
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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