Target-Driven Design of Deep-UV Nonlinear Optical Materials via Interpretable Machine Learning.

The development of a data-driven science paradigm is greatly revolutionizing the process of materials discovery. Particularly, exploring novel nonlinear optical (NLO) materials with the birefringent phase-matching ability to deep-ultraviolet (UV) region is of vital significance for the field of laser technologies. Herein, a target-driven materials design framework combining high-throughput calculations (HTC), crystal structure prediction, and interpretable machine learning (ML) is proposed to accelerate the discovery of deep-UV NLO materials. Using a dataset generated from HTC, an ML regression model for predicting birefringence is developed for the first time, which exhibits a possibility of achieving fast and accurate prediction. Essentially, crystal structures are adopted as the only known input of this model to establish a close structure-property relationship mapping birefringence. Utilizing the ML-predicted birefringence which can affect the shortest phase-matching wavelength, a full list of potential chemical compositions based on an efficient screening strategy is identified. Further, eight structures with good stability are discovered to show potential applications in the deep-UV region, owing to their promising NLO-related properties. This study provides a new insight into the discovery of NLO materials and this design framework can identify desired materials with high performances in the broad chemical space at a low computational cost.