Affiliation:
1. Research Center for Crystal Materials CAS Key Laboratory of Functional Materials and Devices for Special Environments Xinjiang Technical Institute of Physics & Chemistry CAS Xinjiang Key Laboratory of Electronic Information Materials and Devices 40–1 South Beijing Road Urumqi 830011 China
2. Center of Materials Science and Optoelectronics Engineering University of Chinese Academy of Sciences Beijing 100049 China
Abstract
AbstractThe 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.
Funder
West Light Foundation of the Chinese Academy of Sciences
Subject
Mechanical Engineering,Mechanics of Materials,General Materials Science
Cited by
16 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献