Our Technology

The inevitable lattice defects of all natural crystals manifest themselves through the spectral dependence of light absorption in the mid-IR region of the optical spectrum.

By collecting multiple spectral data taken from different parts of the crystal sample, followed by the data analysis using methods of deep machine learning, it is possible to identify spectral markers associated with otherwise unique lattice defect patterns.

The core of the proposed method is a neural network trained on a number of samples belonging to the same class. The class could be multiple measurements of the same diamond or gemstones of the same geographical origin. The trained neural network is able to identify previously untested samples and to predict their origin correctly.

Results of our study suggest that the underlying crystal property allowing its identification is ever- present native defects in the lattice. Three-dimensional pattern of those defects being set by temperature, pressure and their gradients in both time and space during crystal formation seems to bear information about its identity, including its origin. In many aspects, it resembles genomics in living cells, and we call it the gemstone genomics concept.