This invention presents an innovative framework for the application of
machine learning for identification of alloys or composites with desired properties of interest. For each output property of interest, we identify the corresponding driving (input) factors. These input factors may include the material composition, heat treatment, process,
microstructure, temperature,
strain rate, environment or testing mode. Our framework assumes selection of optimization technique suitable for the application at hand and data available, starting with simple linear, or quadratic,
regression analysis. We present a
physics-based model for predicting the
ultimate tensile strength, a model that accounts for physical dependencies, and factors in the underlying
physics as a priori information. In case an
artificial neural network is deemed suitable, we suggest employing custom kernel functions consistent with the underlying
physics, for the purpose of attaining tighter
coupling, better prediction, and extracting the most out of the—usually limited—input data available.