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		题名	作者	年代	出处	被引量
	1	Machine learning for perovskite materials design and discovery显示文摘The development of materials is one of the driving forces to accelerate modern scientific progress and technological innovation.Machine learning(ML)technology is rapidly developed in many fields and opening blueprints for the discovery and rational design of materials.In this review,we retrospected the latest applications of ML in assisting perovskites discovery.First,the development tendency of ML in perovskite materials publications in recent years was organized and analyzed.Second,the workflow of ML in perovskites discovery was introduced.Then the applications of ML in various properties of inorganic perovskites,hybrid organic–inorganic perovskites and double perovskites were briefly reviewed.In the end,we put forward suggestions on the future development prospects of ML in the field of perovskite materials.	Qiuling Tao Pengcheng Xu Minjie Li Wencong Lu	2021	npj Computational Materials2021，，1：	4
	2	Anion order in oxysulfide perovskites:origins and implications显示文摘Heteroanionic oxysulfide perovskite compounds represent an emerging class of new materials allowing for a wide range of tunability in the electronic structure that could lead to a diverse spectrum of novel and improved functionalities.Unlike cation ordered double perovskites—where the origins and design rules of various experimentally observed cation orderings are well known and understood—anion ordering in heteroanionic perovskites remains a largely uncharted territory.	Ghanshyam Pilania Ayana Ghosh Steven T.Hartman Rohan Mishra Christopher R.Stanek Blas P.Uberuaga	2020	npj Computational Materials2020，，1：	0

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