Aiding Material Design Through Machine Learning

Abstract

Machine learning is a field that has been around for decades whose impact and presence continues to increase across scientific and commercial communities. However, until recently, machine learning has not been thought of as a viable methodology that could significantly aid novel material discovery and design. That is, machine learning-aided material design and/or discovery is an emerging research topic, but one that holds immense potential. Such a system could, theoretically, be used to discover novel materials or surfaces that possess desirable properties across the electromagnetic spectrum under specific conditions. Herein, we present our preliminary machine learning- based framework for novel material design and discovery. We emphasize that our proposed framework is in its infancy; however, it is laying the foundation for the discovery of fundamental theories and knowledge for this novel technology. Baseline elementary experiments are presented as a proof-of-concept to show the feasibility of our proposed framework for the task of material design. Specifically, we put forth a multi-stage machine learning framework for new material discovery considering material surface geometries for predicting object signatures in the X band. Our proposed multi-stage framework is structured as follows: 1) a deep neural network (NN) is trained for predicting the time response scattered from an object based upon surface geometries (micro-feature spacing, height, etc.); and 2) a genetic algorithm is used to search for the optimal surface geometry configuration whose predicted scattered response (closely) matches that of a desired object response in the X band.