Network analysis of synthesizable materials discovery

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. Predicting the synthesizability of inorganic materials is challenging due to the many variables and complex phenomena involved in synthesis. Here, the authors combine material stabilities with a historical analysis of experimental discovery timelines as a temporal network to predict the synthesizability of hypothetical materials.

• Publication year

  2018

• Venue

  Nature Communications

• Publication date

  2018-06-14

• Fields of study

  Biology, Materials Science, Physics, Computer Science, Medicine

• Identifiers
  DOI 10.1038/s41467-019-10030-5arXiv 1806.05772PMID 31043603PMCID 6494829
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar, PubMed

• No claims are published for this paper.

• No concepts are published for this paper.

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