An Integrated Machine Learning Workflow Based on Deep Generative Models for Discovery of Inorganic Nonlinear Optical Crystals

The discovery of nonlinear optical (NLO) materials for deep-ultraviolet (DUV) and mid-infrared (MIR) applications remains a significant challenge because of stringent structural and property requirements. While machine learning (ML) has improved the prediction efficiency of NLO properties, its scope is still limited by existing data sets, resulting in insufficient exploration in uncharted chemical spaces. Here, we develop an integrated workflow based on deep generative models to overcome this challenge. Unlike most ML-based property predictors that require target labels collected from existing NLO crystals, the reference values of NLO properties are no longer necessary for training generative models. Thousands of generated structures are subsequently filtered by ML predictors and verified by first-principles calculations. Ultimately, 27 and 13 potential DUV and MIR NLO materials were identified, respectively. These crystals not only satisfy the requirements for NLO applications in the DUV or MIR spectrum but also showcase chemical compositional and structural diversity, paving a new way for the discovery of new NLO-active structural units.