首页|University of Warwick Reports Findings in Machine Learning (Machine Learning Interatomic Potentials for Reactive Hydrogen Dynamics at Metal Surfaces Based on Iterative Refinement of Reaction Probabilities)
University of Warwick Reports Findings in Machine Learning (Machine Learning Interatomic Potentials for Reactive Hydrogen Dynamics at Metal Surfaces Based on Iterative Refinement of Reaction Probabilities)
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 DailyNews Daily News – New research on Machine Learning is the subject of a report. According to newsreporting from Coventry, United Kingdom, by NewsRx journalists, research stated, “The reactive chemistryof molecular hydrogen at surfaces, notably dissociative sticking and hydrogen evolution, plays a crucial rolein energy storage and fuel cells. Theoretical studies can help to decipher underlying mechanisms andreaction design, but studying dynamics at surfaces is computationally challenging due to the complexelectronic structure at interfaces and the high sensitivity of dynamics to reaction barriers.”
NETL
NSTL
