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Study of properties of chemicals, drugs, biomaterials and alloys requiresdecades of dedicated work. Often times the outcome, however, is not what isexpected for practical applications. This research procedure can be inverted bythe new artificial intelligence and optimization methods. Instead of studyingthe properties of a material and its structurally close derivatives, thechemical and structural parameter space that contains all possible derivativesof that material can be scanned in a fast and smart way at the HPC centers. Asa result of this, the particular material that has the specific physical orchemical properties can be found. Here we show how Bayesian optimization,Gaussian regression and artificial neural networks can be used towards thisgoal. We present an example smart search on the doped graphene quantum dotparameter space.

Accelerating nanomaterials discovery with artificial intelligence at the HPC centers