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Augmenting the discovery of computationally complex ceramics for extreme environments with machine learning

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Abstract

We present a high-throughput, material-agnostic strategy to discover new compositionally complex ceramics (C3) for extreme environments by utilizing machine learning (ML) techniques to predict the stoichiometries and properties of structures within a given design space. This example study focuses on a well-understood design space (Si–C–N) so that predictions may be validated. Evolutionary structure searches coupled with density functional theory (DFT) calculations are applied to find structures with low energies (i.e., lying on or close to the convex hull), while also maximizing a targeted property (in this case, hardness). The structure–property relationship data obtained throughout these searches are exploited in ML algorithms to create an accurate and efficient surrogate model of the energy and hardness landscapes. The ML models serve to screen structures with optimal attributes and reduce computational costs associated with the property calculations, thereby accelerating the discovery of new structures and stoichiometries with desired traits.

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Data availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Funding

The research was sponsored by the University of Florida Artificial Intelligence Research Catalyst Fund.

Author information

Authors and Affiliations

  1. Department of Material Science and Engineering, University of Florida, Gainesville, FL, 32611, USA

    Salil Bavdekar & Richard G. Hennig

  2. Quantum Theory Project, University of Florida, Gainesville, FL, 32611, USA

    Richard G. Hennig

  3. Department of Mechanical and Aerospace Engineering, University of Florida, Gainesville, FL, 32611, USA

    Ghatu Subhash

Authors
  1. Salil Bavdekar
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  2. Richard G. Hennig
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  3. Ghatu Subhash
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Contributions

SB contributed toward conceptualization, data curation, formal analysis, funding acquisition, methodology, visualization, and writing—original draft. GS contributed toward conceptualization, project administration, resources, supervision, and writing—review & editing. RH contributed toward conceptualization, funding acquisition, software, supervision, and writing—review & editing.

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Correspondence to Ghatu Subhash.

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Bavdekar, S., Hennig, R.G. & Subhash, G. Augmenting the discovery of computationally complex ceramics for extreme environments with machine learning. Journal of Materials Research 38, 5055–5064 (2023). https://doi.org/10.1557/s43578-023-01217-0

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  • DOI: https://doi.org/10.1557/s43578-023-01217-0

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