Materials Synthesis Insights from Scientific Literature via Text Extraction and Machine Learning
Open accessAbstract
In the past several years, Materials Genome Initiative (MGI) efforts have produced myriad examples of computationally designed materials in the fields of energy storage, catalysis, thermoelectrics, and hydrogen storage as well as large data resources that are used to screen for potentially transformative compounds. The bottleneck in high-throughput materials design has thus shifted to materials synthesis, which motivates our development of a methodology to automatically compile materials synthesis parameters across tens of thousands of scholarly publications using natural language processing techniques. To demonstrate our framework’s capabilities, we examine the synthesis conditions for various metal oxides across more than 12 thousand manuscripts. We then apply machine learning methods to predict the critical parameters needed to synthesize titania nanotubes via hydrothermal methods and verify this result against known mechanisms. Finally, we demonstrate the capacity for transfer learning by using machine learning models to predict synthesis outcomes on materials systems not included in the training set and thereby outperform heuristic strategies.
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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 [14]
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- Authors
- Kim, E., Huang, K., Saunders, A., McCallum, A., Ceder, G., & Olivetti, E.
- Affiliations
- Massachusetts Institute of Technology (US)
- Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
- University of Massachusetts Amherst (US)
- University of California, Berkeley (US)
- Corresponding author
- Olivetti, Elsa
- Venue
- Chemistry of Materials
- Publisher
- American Chemical Society (ACS)
- Published
- 2017-10-19
- Volume
- 29
- Issue
- 21
- Pages
- 9436-9444
- ISSN
- 0897-4756 (ISSN-L)
- 1520-5002
- OpenAlex
- W2766362701
- Cited by
- 473
- References
- 58
- Citations by year
- 2026: 32 · 2025: 64 · 2024: 41 · 2023: 60 · 2022: 71 · 2021: 80 · 2020: 54 · 2019: 55 · 2018: 16
- FWCI
- 14.44
- Type
- journal article
- Language
- en
- Open access
- green
- www.osti.gov
- Licence
- other-oa
- Locations
- Chemistry of Materials (publishedVersion)
- OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion, other-oa)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc)
- eScholarship (California Digital Library) (submittedVersion)
- Funders
- U.S. Department of Energy (EDCBEE)
- Massachusetts Institute of Technology (MIT Energy Initiative)
- U.S. Department of Defense (N00014-16-1-2432)
- Government of Canada
- National Science Foundation (1534340)
- Topics
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Social Sciences › General Social Sciences › Computational and Text Analysis Methods
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Keywords
- materials synthesis, Materials Genome Initiative, natural language processing, metal oxides, TiO2 nanotubes, hydrothermal synthesis, transfer learning, high-throughput materials design, computational materials design
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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