摘要
In nature,the properties of matter are ultimately governed by the electronic structures.Quantum chemistry(QC)at electronic level matches well with a few simple physical assumptions in solving simple problems.To date,machine learning(ML)algorithm has been migrated to this field to simplify calculations and improve fidelity.This review introduces the basic information on universal electron structures of emerging energy materials and ML algorithms involved in the prediction of material properties.Then,the structure-property relationships based on ML algorithm and QC theory are reviewed.Especially,the summary of recently reported applications on classifying crystal structure,modeling electronic structure,optimizing experimental method,and predicting performance is provided.Last,an outlook on ML assisted QC calculation towards identifying emerging energy materials is also presented.
基金
supported by the National Natural Science Foundation of China(grant number 51872157)
Shenzhen Technical Plan Project(grant number KQJSCX20160226191136 and JCYJ20170412170911187)
Research Grants Council of the Hong Kong Special Administrative Region,China[grant number PF17-10186]。
