CrysXPP:An explainable property predictor for crystalline materials  

作　　者：Kishalay Das Bidisha Samanta Pawan Goyal Seung-Cheol Lee Satadeep Bhattacharjee Niloy Ganguly 

机构地区：[1]Indian Institute of Technology Kharagpur,Kharagpur,India [2]Indo Korea Science and Technology Center,Bangalore,India [3]Leibniz University of Hannover,Hannover,Germany

出　　处：《npj Computational Materials》2022年第1期424-434,共11页计算材料学（英文）

摘　　要：We present a deep-learning framework,CrysXPP,to allow rapid and accurate prediction of electronic,magnetic,and elastic properties of a wide range of materials.CrysXPP lowers the need for large property tagged datasets by intelligently designing an autoencoder,CrysAE.The important structural and chemical properties captured by CrysAE from a large amount of available crystal graphs data helped in achieving low prediction errors.Moreover,we design a feature selector that helps to interpret the model’s prediction.Most notably,when given a small amount of experimental data,CrysXPP is consistently able to outperform conventional DFT.A detailed ablation study establishes the importance of different design steps.We release the large pre-trained model CrysAE.We believe by fine-tuning the model with a small amount of property-tagged data,researchers can achieve superior performance on various applications with a restricted data source.

关 键 词：materials. PROPERTY CRYSTALLINE 

分 类 号：O15[理学—数学]