A generative deep learning framework for inverse design of compositionally complex bulk metallic glasses

Abstract The design of bulk metallic glasses (BMGs) via machine learning (ML) has been a topic of active research recently. However, the prior ML models were mostly built upon supervised learning algorithms with human inputs to navigate the high dimensional compositional space, which becomes inefficient with the increasing compositional complexity in BMGs. Here, we develop a generative deep-learning framework to directly generate compositionally complex BMGs, such as high entropy BMGs. Our framework is built on the unsupervised Generative Adversarial Network (GAN) algorithm for data generation and the supervised Boosted Trees algorithm for data evaluation. We studied systematically the confounding effect of various data descriptors and the literature data on the effectiveness of our framework both numerically and experimentally. Most importantly, we demonstrate that our generative deep learning framework is capable of producing composition-property mappings, therefore paving the way for the inverse design of BMGs.