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Thermal conductivity and balanced performance in infrared nonlinear optical multicomponent chalcogenides Li_(x)Ag_(1-x)Ga_(y)In_(1-y)Se_(2)
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作者 L.I.Isaenko Bohui Xu +4 位作者 K.E.Korzhneva Pifu Gong d.a.samoshkin A.F.Kurus Zheshuai Lin 《Inorganic Chemistry Frontiers》 2025年第5期1867-1873,共7页
The performance of infrared(IR)nonlinear optical(NLO)materials is significantly affected by the thermal conductivity kL,but studies on the structure and property relationship of kL in these materials are very rare.In ... The performance of infrared(IR)nonlinear optical(NLO)materials is significantly affected by the thermal conductivity kL,but studies on the structure and property relationship of kL in these materials are very rare.In this work we evaluated the k_(L) in IR NLO multicomponent chalcogenides Li_(x)Ag_(1-x)Ga_(y)In_(1-y)Se_(2) with a smooth change in the compositions x and y by using a machine learning approach and laser flash measurements,combined with available experimental results.The found patterns of kL dependence on the atomic mass,bond length and electronegativity provide an effective understanding for navigation in the process of searching for new chalcogenide crystals with an optimal set of parameters that allow them to be effectively used as a frequency converter of laser radiation in the IR range.Moreover,the compositions Li_(0.5)Ag_(0.5)GaSe_(2),Li_(0.81)Ag_(0.19)InSe_(2) and AgGa_(0.5)In_(0.5)Se_(2) are demonstrated to exhibit a balanced combination of the parameters kL,NLOeffects,energy band gaps,and birefringence for IR NLO applications. 展开更多
关键词 machine learning machine learning approach infrared nonlinear optical materials laser flash measurements ir nlo multicomponent chalcogenides chalcogenides atomic mass thermal conductivity
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