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.
Center of Materials Science and Optoelectronics Engineering, University of Chinese Academy of Sciences, Beijing, 100049, China.
Adv Mater. 2023 Jun;35(23):e2300848. doi: 10.1002/adma.202300848. Epub 2023 Apr 25.
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.
数据驱动科学范式的发展正在极大地改变材料发现的过程。特别是，探索具有双折射相位匹配能力的新型非线性光学（NLO）材料，对于激光技术领域至关重要。在此，我们提出了一个基于高通量计算（HTC）、晶体结构预测和可解释机器学习（ML）的目标驱动材料设计框架，以加速深紫外 NLO 材料的发现。通过使用 HTC 生成的数据集，首次开发了用于预测双折射的 ML 回归模型，该模型表现出实现快速准确预测的可能性。本质上，该模型仅将晶体结构作为唯一已知输入，以建立双折射与结构之间的紧密关系映射。利用 ML 预测的双折射值，该值可以影响最短的相位匹配波长，基于有效的筛选策略确定了潜在化学成分的完整列表。此外，由于具有良好的 NLO 相关性质，还发现了八种具有良好稳定性的结构，它们在深紫外区域具有潜在的应用前景。这项研究为 NLO 材料的发现提供了新的见解，并且该设计框架可以以较低的计算成本在广泛的化学空间中识别具有高性能的所需材料。
