We have developed a machine learning algorithm that identifies interatomic potentials using the results of ab initio calculations as training data. This algorithm enables us to predict the catalytic activity of nanoparticle surfaces (up to 3,000 atoms) and the lithium (Li) diffusion behavior accompanying the structural phase transition of solid Li ion conductors.
MATERIALS INFORMATICS RESEARCH
Conventional materials development requires years and years of repeating try-and-error experiments for enormous number of combinations to find one practical result.
The Materials Informatics Research Domain introduces data analytics to materials development, working with big data learning and multiscale materials simulations to predict candidate materials with the desired performance. Through this approach, we are aiming to greatly shorten resources needed for the materials development, and facilitate the discovery of new materials.