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دسته بندی:
یادگیری ماشین - machine learning
سال انتشار:
2019
عنوان انگلیسی مقاله:
Discovering unusual structures from exception using big data and machine learning techniques
ترجمه فارسی عنوان مقاله:
کشف ساختارهای غیر معمول از استثناء با استفاده از داده های بزرگ و تکنیک های یادگیری ماشین
منبع:
Sciencedirect - Elsevier - Science Bulletin, 64 (2019) 612-616: doi:10:1016/j:scib:2019:04:015
نویسنده:
Jianshu Jie a, Zongxiang Hua, Guoyu Qian a, Mouyi Wenga, Shunning Li a, Shucheng Li a, Mingyu Hua, Dong Chen a, Weiji Xiao a, Jiaxin Zheng a, Lin-Wang Wangb,⇑, Feng Pan
چکیده انگلیسی:
Recently, machine learning (ML) has become a widely used technique in materials science study. Most
work focuses on predicting the rule and overall trend by building a machine learning model. However,
new insights are often learnt from exceptions against the overall trend. In this work, we demonstrate that
how unusual structures are discovered from exceptions when machine learning is used to get the relationship
between atomic and electronic structures based on big data from high-throughput calculation
database. For example, after training an ML model for the relationship between atomic and electronic
structures of crystals, we find AgO2F, an unusual structure with both Ag3+ and O2 2 , from structures whose
band gap deviates much from the prediction made by our model. A further investigation on this structure
might shed light into the research on anionic redox in transition metal oxides of Li-ion batterie.
Keywords: Machine learning | Gradient boosting decision tree | Band gap | Unusual structures
قیمت: رایگان
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