Leveraging General Adversarial Networks for Material Sciences

Material scientists often face the challenge of figuring out how to effectively search the vast chemical design space to locate the materials with their desired properties. To address this challenge, many scientists have turned to artificial intelligence in the race to discover new and advanced materials. A general adversarial network is a variety of machine learning framework that leverages the idea of "adversarial training" where a network is trained on adversarial examples. It is an idea that originates from game theory and introduced to the machine learning community in 2014 by Ian J. Goodfellow. With this in mind, a targeted strategy for developing novel chemical compositions is to develop sampling algorithms that can exploit both explicit chemical knowledge and implicit rules of composition embodied in a large database of materials.