Machine learning-guided property prediction of energetic materials: Recent advances, challenges, and perspectives
Xiaolan Tian(Southwest University of Science and Technology), Qinghua Zhang(Wuhan College), Xiujuan Qi(Southwest University of Science and Technology), Siwei Song(China Academy of Engineering Physics), Yi Wang(Nanjing Hydraulic Research Institute), Fang Chen(China Academy of Engineering Physics)
Cited by 88
Related Papers
Transfer of Fresh versus Frozen Embryos in Ovulatory Women
|New England Journal of Medicine|2018|514
Accelerating the discovery of insensitive high-energy-density materials by a materials genome approach
|Nature Communications|2018|395
Dynamic Behavior of Molecular Switches in Crystal under Pressure and Its Reflection on Tactile Sensing
|Journal of the American Chemical Society|2014|220
Graphene-Draped Semiconductors for Enhanced Photocorrosion Resistance and Photocatalytic Properties
|Journal of the American Chemical Society|2017|194
A kinetic model for thermally induced hydrogen and carbon isotope fractionation of individual n-alkanes in crude oil
|Geochimica et Cosmochimica Acta|2005|168