文章基本信息

标题：Accelerating materials property predictions using machine learning
本地全文：下载
作者：Ghanshyam Pilania ; Chenchen Wang ; Xun Jiang 等
期刊名称：Scientific Reports
电子版ISSN：2045-2322
出版年度：2013
卷号：3
期号：1
DOI：10.1038/srep02810
语种：English
出版社：Springer Nature
摘要：The materials discovery process can be significantly expedited and simplified if we can learn effectively from available knowledge and data. In the present contribution, we show that efficient and accurate prediction of a diverse set of properties of material systems is possible by employing machine (or statistical) learning methods trained on quantum mechanical computations in combination with the notions of chemical similarity. Using a family of one-dimensional chain systems, we present a general formalism that allows us to discover decision rules that establish a mapping between easily accessible attributes of a system and its properties. It is shown that fingerprints based on either chemo-structural (compositional and configurational information) or the electronic charge density distribution can be used to make ultra-fast, yet accurate, property predictions. Harnessing such learning paradigms extends recent efforts to systematically explore and mine vast chemical spaces, and can significantly accelerate the discovery of new application-specific materials.