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

Abstract 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.