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Cited by 120Machine Learning in Materials ScienceComputational Drug Discovery MethodsCrystal Structures and Properties

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Network analysis of synthesizable materials discovery

Open access
Aykol, Muratahan, Hegde, Vinay I., Hung, Linda, Suram, Santosh, Herring, Patrick, Wolverton, Chris, Hummelshøj, Jens S.·2019·Nature Communications
DOI 10.1038/s41467-019-10030-5PMC PMC6494829
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Abstract

Assessing the synthesizability of inorganic materials is a grand challenge for accelerating their discovery using computations. Synthesis of a material is a complex process that depends not only on its thermodynamic stability with respect to others, but also on factors from kinetics, to advances in synthesis techniques, to the availability of precursors. This complexity makes the development of a general theory or first-principles approach to synthesizability currently impractical. Here we show how an alternative pathway to predicting synthesizability emerges from the dynamics of the materials stability network: a scale-free network constructed by combining the convex free-energy surface of inorganic materials computed by high-throughput density functional theory and their experimental discovery timelines extracted from citations. The time-evolution of the underlying network properties allows us to use machine-learning to predict the likelihood that hypothetical, computer-generated materials will be amenable to successful experimental synthesis.

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  • Combining ab initio calculations with simple substitutions has allowed researchers to improve to 48,000 computationally stable materials according to the paper's own recalculations.

    “Combining ab initio calculations with simple substitutions has allowed researchers to improve to 48,000 computationally stable materials according to our own recalculations22–24 (see Methods).”

    From Scaling deep learning for materials discovery (2023) · cites this paper as [24]

    The Introduction and Methods describe high-throughput ab initio calculations used to assess the stability of inorganic materials. The Results report approximately 22,000 stable materials discovered, not 48,000; the paper does not substantiate the claimed total or its derivation through simple substitutions. In the printed citing sentence, “our own recalculations” refers to the citing authors, not this source. (LLM, conf=0.84)

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Research works printed but not linked: 35

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Metadata

Authors
Aykol, M., Hegde, V. I., Hung, L., Suram, S., Herring, P., Wolverton, C., & Hummelshøj, J. S.
Affiliations
  • Toyota Research Institute (US)
  • Northwestern University (US)
Corresponding author
Aykol, Muratahan
ORCID
Venue
Nature Communications
Publisher
Springer Science and Business Media LLC
Published
2019-05-01
Volume
10
Issue
1
Pages
2018-2018
ISSN
2041-1723 (ISSN-L)
DOI
10.1038/s41467-019-10030-5
PMC
PMC6494829
OpenAlex
W2808135796
Cited by
120
References
4
Citations by year
2026: 15 · 2025: 11 · 2024: 15 · 2023: 16 · 2022: 17 · 2021: 21 · 2020: 15 · 2019: 9
FWCI
5.43
Type
journal article
Language
en
Open access
Licence
Locations
  • Nature Communications (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • arXiv (Cornell University) (submittedVersion, other-oa)
  • RePEc: Research Papers in Economics (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • arXiv (Cornell University)
Funders
  • National Science Foundation (DMR-1720,139, 1720139)
  • Toyota Research Institute
  • Materials Research Science and Engineering Center, Harvard University (1720139, DMR-1720139)
  • Division of Materials Research (DMR-1720139, 1720139)
Topics
  • Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
  • Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
  • Physical Sciences › Materials Science › Electronic, Optical and Magnetic Materials › Crystal Structures and Properties
Keywords
synthesizability, scale-free networks, high-throughput density functional theory, machine learning prediction, materials synthesis, thermodynamic stability

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