A machine learning(ML)potential for Au clusters is developed through training on a dataset including several different sized clusters.This ML potential accurately covers the whole configuration space of Au clusters in...A machine learning(ML)potential for Au clusters is developed through training on a dataset including several different sized clusters.This ML potential accurately covers the whole configuration space of Au clusters in a broad size range,thus expressing a good performance in search of their global minimum energy structures.Based on our potential,the low-lying structures of 17 different sized Au clusters are identified,which shows that small sized Au clusters tend to form planar structures while large ones are more likely to be stereo,revealing the critical size for the two-dimensional(2D)to three-dimensional(3D)structural transition.Our calculations demonstrate that ML is indeed powerful in describing the interaction of Au atoms and provides a new paradigm on accelerating the search of structures.展开更多
Based on first-principles density functional theory calculation,we discover a novel form of spin-orbit(SO)splitting in two-dimensional(2D)heterostructures composed of a single Bi(111)bilayer stacking with a 2D semicon...Based on first-principles density functional theory calculation,we discover a novel form of spin-orbit(SO)splitting in two-dimensional(2D)heterostructures composed of a single Bi(111)bilayer stacking with a 2D semiconducting In_(2)Se_(2) or a 2D ferroelectricα-In_(2)Se_(3) layer.Such SO splitting has a Rashba-like but distinct spin texture in the valence band around the maximum,where the chirality of the spin texture reverses within the upper spin-split branch,in contrast to the conventional Rashba systems where the upper branch and lower branch have opposite chirality solely in the region below the band crossing point.The ferroelectric nature ofα-In_(2)Se_(3) further enables the tuning of the spin texture upon the reversal of the electric polarization with the application of an external electric field.Detailed analysis based on a tight-binding model reveals that such SO splitting texture results from the interplay of complex orbital characters and substrate interaction.This finding enriches the diversity of SO splitting systems and is also expected to promise for spintronic applications.展开更多
基金supported by the National Key Research and Development Program of China(No.2017YFA0204904)the Fundamental Research Funds for the Central Universities,China(No.WK3510000013)the National Natural Science Foundation of China(No.61922073).
基金Computational support was provided by Supercomputing Center in USTC and National Supercomputing Center in Tianjinthe National Key Research and Development Program of China(Grant Nos.2017YFA0204904 and 2019YFA0210004)。
文摘A machine learning(ML)potential for Au clusters is developed through training on a dataset including several different sized clusters.This ML potential accurately covers the whole configuration space of Au clusters in a broad size range,thus expressing a good performance in search of their global minimum energy structures.Based on our potential,the low-lying structures of 17 different sized Au clusters are identified,which shows that small sized Au clusters tend to form planar structures while large ones are more likely to be stereo,revealing the critical size for the two-dimensional(2D)to three-dimensional(3D)structural transition.Our calculations demonstrate that ML is indeed powerful in describing the interaction of Au atoms and provides a new paradigm on accelerating the search of structures.
基金Project supported by the Science Fund from the Ministry of Science and Technology of China(Grant Nos.2017YFA0204904 and 2019YFA0210004)the National Natural Science Foundation of China(Grant Nos.11674299 and 11634011)+2 种基金the Strategic Priority Research Program of the Chinese Academy of Sciences(Grant No.XDB30000000)the Fund of Anhui Initiative Program in Quantum Information Technologies(Grant No.AHY170000)the Fundamental Research Funds for the Central Universities,China(Grant No.WK3510000013).
文摘Based on first-principles density functional theory calculation,we discover a novel form of spin-orbit(SO)splitting in two-dimensional(2D)heterostructures composed of a single Bi(111)bilayer stacking with a 2D semiconducting In_(2)Se_(2) or a 2D ferroelectricα-In_(2)Se_(3) layer.Such SO splitting has a Rashba-like but distinct spin texture in the valence band around the maximum,where the chirality of the spin texture reverses within the upper spin-split branch,in contrast to the conventional Rashba systems where the upper branch and lower branch have opposite chirality solely in the region below the band crossing point.The ferroelectric nature ofα-In_(2)Se_(3) further enables the tuning of the spin texture upon the reversal of the electric polarization with the application of an external electric field.Detailed analysis based on a tight-binding model reveals that such SO splitting texture results from the interplay of complex orbital characters and substrate interaction.This finding enriches the diversity of SO splitting systems and is also expected to promise for spintronic applications.
