We designed a deep learning algorithm to automate the identification of inorganic materials from experimentally measured X-ray diffraction spectra.
Significance and Impact
The methods presented here may be integrated in experimental workflows to facilitate the high-throughput and autonomous discovery of inorganic materials.
- Simulated spectra were augmented with possible experimental artifacts left over from sample preparation and synthesis.
- Off-stoichiometry was accounted for by simulating hypothetical solid solutions between known experimental phases.
- To handle multi-phase mixtures, a branching algorithm was developed to identify the set of compounds that maximize the average probability of predictions given by the deep learning model.