Graph neural network guided design of novel deep-ultraviolet optical materials with high birefringence  

图神经网络指导新型深紫外大双折射晶体材料的设计

作　　者：Ivan A.Kruglov Liudmila A.Bereznikova Congwei Xie Dongdong Chu Ke Li Evgenii Tikhonov Abudukadi Tudi Arslan Mazitov Min Zhang Shilie Pan Zhihua Yang 克鲁格洛夫伊万;柳德米拉别列兹尼科娃;谢聪伟;储冬冬;李珂;吉洪诺夫叶夫格尼;阿布都卡地吐地;阿尔斯兰马齐托夫;张敏;潘世烈;杨志华

机构地区：[1]Research Center for Crystal Materials,State Key Laboratory of Functional Materials and Devices for Special Environmental Conditions,Xinjiang Key Laboratory of Functional Crystal Materials,Xinjiang Technical Institute of Physics and Chemistry,Chinese Academy of Sciences,Urumqi 830011,China [2]Moscow Institute of Physics and Technology,9 Institutsky Lane,Dolgoprudny 141700,Russia [3]Emerging Technologies Research Center,XPANCEO,Internet City,Emmay Tower,Dubai,United Arab Emirates

出　　处：《Science China Materials》2024年第12期3941-3947,共7页中国科学（材料科学）（英文版）

基　　金：supported by the National Key Research and Development Program of China(2021YFB3601501);the Key Research Program of Frontier Sciences,Chinese Academy of Scineces(CAS,ZDBSLY-SLH035);the National Natural Science Foundation of China(22193044,61835014,and 51972336);West Light Foundation of CAS(2019-YDYLTD002);the Natural Science Foundation of Xinjiang(2021D01E05);CAS Project for Young Scientists in Basic Research(YSBR-024);Xinjiang Major Science and Technology Project(2021A01001);CAS President’s International Fellowship Initiative(PIFI,2020PM0046);Tianshan Basic Research Talents(2022TSYCJU0001);Kruglov IA and Bereznikova LA thank the Ministry of Science and Higher Education of the Russian Federation(FSMG2021-0005)。

摘　　要：Finding crystals with high birefringence(Δn),especially in deep-ultraviolet(DUV)regions,is important for developing polarization devices such as optical fiber sensors.Such materials are usually discovered using experimental techniques,which are costly and inefficient for a large-scale screening.Herein,we collected a database of crystal structures and their optical properties and trained atomistic line graph neural network to predict theirΔn.To estimate the level of confidence of the trained model on new data,D-optimality criterion was implemented.Using trained graph neural network,we searched for novel materials with highΔn in the Materials Project database and discovered two new DUV birefringent candidates:NaYCO_(3)F_(2) and SClO_(2)F,with highΔn values of 0.202 and 0.101 at 1064 nm,respectively.Further analysis reveals that strongly anisotropic units with various anions andπ-conjugated planar groups are beneficial for highΔn.寻找具有大双折射率(Δn)的晶体材料,尤其是深紫外大双折射晶体材料,对于制备光纤传感器等偏振器件非常重要.具有大双折射率的晶体材料通常需借助实验技术发现.然而,大量耗时的实验并不利于高效寻找具有大双折射率的晶体材料.在本文中,我们收集了一个包含数千晶体结构及其光学性质的数据集,并采用原子线图神经网络(ALIGNN)训练了可用于快速预测晶体材料双折射率的机器学习模型.我们采用D-optimality准则评估所构建机器学习模型的预测可信度.基于该双折射率机器学习模型,我们从Materials Project数据库中搜索了具有大双折射率的晶体材料,发现了两种新型深紫外大双折射候选材料NaYCO_(3)F_(2)和SClO_(2)F,它们的双折射率分别为0.202和0.101@1064 nm.进一步分析表明,具有强各向异性的多阴离子基团和π共轭平面基团有利于产生大的双折射率.

关 键 词：machine learning BIREFRINGENCE optical materials D-OPTIMALITY 

分 类 号：TB30[一般工业技术—材料科学与工程] TP183[自动化与计算机技术—控制理论与控制工程]