The AI model was utilized to calculate the hardness of the carbon species generated using XtalOpt – an open -source algorithm for crystal structure prediction developed in one of the participating research labs. The AI model for assessing hardness was trained utilizing the Automatic FLOW (AFLOW) database, a library of materials with properties that have been determined.
This procedure accelerates the material development significantly. The calculations still take significant amount of time, however, once the AI model is trained, the hardness will be predicted very fast and with reasonable accuracy.
Further information: Patrick Avery et al, Predicting superhard materials via a machine learning informed evolutionary structure search, npj Computational Materials (2019). DOI: 10.1038/s41524-019-0226-8
Introduction to AI and ML Right from mobile phone apps which recommend our favorite music to the most sophisticated autonomous vehicles, every device in today’s times is embedded with artificial intelligence and machine learning. Taking […]
Diamond coatings are commonly used today to protect tools and machine components that are subjected to high wear, and thus to extend their service life. It is known that rubbing two dry diamond surfaces together […]
Ever wondered what makes ice so special to skate on it? Apart from freezing your favorite beverages, it helps make life adventurous. Yes, I am talking about sports like ice skating and ice hockey, it’s […]