Deep learning algorithm emerges as a new method to take the raw features from large dataset and mine their deep implicit relations,which is promising for solving traditional physical challenges.A particularly intricat...Deep learning algorithm emerges as a new method to take the raw features from large dataset and mine their deep implicit relations,which is promising for solving traditional physical challenges.A particularly intricate and difficult challenge is the energy loss mechanism of energetic ions in solid,where accurate prediction of stopping power is a longtime problem.In this work,we develop a deep-learning-based stopping power model with high overall accuracy,and overcome the long-standing deficiency of the existing classical models by improving the predictive accuracy of stopping power for ultra-heavy ion with low energy,and the corresponding projected range.This electronic stopping power model,based on deep learning algorithm,could be hopefully applied for the study of ion-solid interaction mechanism and enormous relevant applications.展开更多
The tantalum arsenide （TaAs） is a topological Weyl semimetal which is a class of materials of gapless with three- dimensional topological structure. In order to develop a comprehensive description of the topological...The tantalum arsenide （TaAs） is a topological Weyl semimetal which is a class of materials of gapless with three- dimensional topological structure. In order to develop a comprehensive description of the topological properties of the Weyl semimetal, we use the density functional theory to study several defects of TaAs after H irradiation and report the electronic dispersion curves and the density of states of these defects. We find that various defects have different influences on the topological properties. Interstitial H atom can shift the Fermi level. Both Ta vacancy with a concentration of 1/64 and As vacancy with a concentration of 1/64 destruct a part of the Weyl points. The substitutional H atom on a Ta site could repair only a part of the Weyl points, while H atom on an As site could repair all the Wevl points.展开更多
基金the National Natural Science Foundation of China(Grant Nos.12135002 and 11705010)the China Postdoctoral Science Foundation(Grant No.2019M650351)the Science Challenge Project(Grant No.TZ2018004)。
文摘Deep learning algorithm emerges as a new method to take the raw features from large dataset and mine their deep implicit relations,which is promising for solving traditional physical challenges.A particularly intricate and difficult challenge is the energy loss mechanism of energetic ions in solid,where accurate prediction of stopping power is a longtime problem.In this work,we develop a deep-learning-based stopping power model with high overall accuracy,and overcome the long-standing deficiency of the existing classical models by improving the predictive accuracy of stopping power for ultra-heavy ion with low energy,and the corresponding projected range.This electronic stopping power model,based on deep learning algorithm,could be hopefully applied for the study of ion-solid interaction mechanism and enormous relevant applications.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.11635003,11025524,and 11161130520)the National Basic Research Program of China(Grant No.2010CB832903)the European Commissions of 7th Framework Programme(FP7-PEOPLE-2010-IRSES)(Grant No.269131)
文摘The tantalum arsenide （TaAs） is a topological Weyl semimetal which is a class of materials of gapless with three- dimensional topological structure. In order to develop a comprehensive description of the topological properties of the Weyl semimetal, we use the density functional theory to study several defects of TaAs after H irradiation and report the electronic dispersion curves and the density of states of these defects. We find that various defects have different influences on the topological properties. Interstitial H atom can shift the Fermi level. Both Ta vacancy with a concentration of 1/64 and As vacancy with a concentration of 1/64 destruct a part of the Weyl points. The substitutional H atom on a Ta site could repair only a part of the Weyl points, while H atom on an As site could repair all the Wevl points.
