Abstract: A method for predicting at least one property of a crystal of a material, with the crystal exhibiting at least one point defect. At least one module samples structures of materials and point defects of the crystal. The method comprises: providing a neural network. The neural network comprises: receiving as input a structure of the material and an ideal crystal unit cell structure. The neural network also comprises: outputting at least one target quantity and using a generated set of data and representing the point defect. The point defect represents a set of coordinates and a type of the point defect and a crystal unit cell structure. The neural network further comprises: receiving as input at least one of a cloud of defect points and a global state vector. The neural network additionally comprises outputting a vector.

Abstract: There is disclosed a method and system for operating a storage area network (SAN) simulator. The method comprises generating training data representative of a SAN system. The method also comprises generating a SAN simulator corresponding to the SAN system, where the SAN simulator outputs a predicted metric of at least one component of the SAN system. The method then comprises using the training data to train a machine learning algorithm (MLA) to determine adjustments for parameters of the SAN simulator.