Element selection for crystalline inorganic solid discovery guided by unsupervised machine learning of experimentally explored chemistry

Andrij Vasylenko, Jacinthe Gamon, Benjamin B. Duff, Vladimir V. Gusev, Luke M. Daniels, Marco Zanella, J. Felix Shin, Paul M. Sharp, Alexandra Morscher, Ruiyong Chen, Alex R. Neale, Laurence J. Hardwick, John B. Claridge, Frédéric Blanc, Michael W. Gaultois, Matthew S. Dyer and Matthew J. Rosseinsky ()
Additional contact information
Andrij Vasylenko: University of Liverpool
Jacinthe Gamon: University of Liverpool
Benjamin B. Duff: University of Liverpool
Vladimir V. Gusev: University of Liverpool
Luke M. Daniels: University of Liverpool
Marco Zanella: University of Liverpool
J. Felix Shin: University of Liverpool
Paul M. Sharp: University of Liverpool
Alexandra Morscher: University of Liverpool
Ruiyong Chen: University of Liverpool
Alex R. Neale: University of Liverpool
Laurence J. Hardwick: University of Liverpool
John B. Claridge: University of Liverpool
Frédéric Blanc: University of Liverpool
Michael W. Gaultois: University of Liverpool
Matthew S. Dyer: University of Liverpool
Matthew J. Rosseinsky: University of Liverpool

Nature Communications, 2021, vol. 12, issue 1, 1-12

Abstract: Abstract The selection of the elements to combine delimits the possible outcomes of synthetic chemistry because it determines the range of compositions and structures, and thus properties, that can arise. For example, in the solid state, the elemental components of a phase field will determine the likelihood of finding a new crystalline material. Researchers make these choices based on their understanding of chemical structure and bonding. Extensive data are available on those element combinations that produce synthetically isolable materials, but it is difficult to assimilate the scale of this information to guide selection from the diversity of potential new chemistries. Here, we show that unsupervised machine learning captures the complex patterns of similarity between element combinations that afford reported crystalline inorganic materials. This model guides prioritisation of quaternary phase fields containing two anions for synthetic exploration to identify lithium solid electrolytes in a collaborative workflow that leads to the discovery of Li3.3SnS3.3Cl0.7. The interstitial site occupancy combination in this defect stuffed wurtzite enables a low-barrier ion transport pathway in hexagonal close-packing.

Date: 2021
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DOI: 10.1038/s41467-021-25343-7

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