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

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

SUBMITTER: Vasylenko A 

PROVIDER: S-EPMC8455628 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature