首页|Investigators at National University of Science & Technology MISiS Discuss Findings in Machine Learning (Machine Learning Potential To Model the D iamond Phase Nucleation In Misoriented Bilayer Graphene)
Investigators at National University of Science & Technology MISiS Discuss Findings in Machine Learning (Machine Learning Potential To Model the D iamond Phase Nucleation In Misoriented Bilayer Graphene)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Data detailed on Machine Learning have been presented. According to news reporting originating from Moscow, Russia, by NewsRx correspondents, research stated, "The machine learning potential (MLP) i s proposed based on the representation of the environment through moment tensors to model the diamond phase nucleation in misoriented bilayer graphene. MLP is t rained on a set of graphene structures, 2D diamond, and their hydrogenated modif ications obtained by density functional theory computations." Financial support for this research came from Russian Science Foundation (RSF). Our news editors obtained a quote from the research from the National University of Science & Technology MISiS, "Learned MLP accurately reproduces energies and strengths of these structures and correctly describes hydrogenatio n of bilayer graphene and the formation of interlayer bonds. Growth of the diamo nd phase in bigraphene with a 5 degrees misorientation of layers is studied usin g MLP. It is found that the formation energy increases with an increase in the n umber of hydrogen atoms, which indicates hydrogen cluster nucleation on the surf ace of bilayer graphene. Hydrogenation of the system leads to the growth of the cubic diamond region up to the AA ‘ stacking promoting the formation of lonsdale ite with the (10 (1) over bar0) surface."
MoscowRussiaCyborgsEmerging Techno logiesMachine LearningNational University of Science & Technol ogy MISiS
NETL
NSTL
