We report the discovery of a rule-breaking two-dimensional aluminum boride (AlB 6 -ptAl-array) nanosheet with a planar tetracoordinate aluminum (ptAl) array in a tetragonal lattice by comprehensive crystal structure search, first-principles calculations, and molecular dynamics simulations. It is a brand new 2D material with a unique motif, high stability, and exotic properties. These anti-van't Hoff/Le Bel ptAl-arrays are arranged in a highly ordered way and connected by two sheets of boron rhomboidal strips above and below the array. The regular alignment and strong bonding between the constituents of this material lead to very strong mechanical strength (in-plane Young's modulus Y x = 379, Y y = 437 N/m, much larger than that of graphene, Y = 340 N/m) and high thermal stability (the framework survived simulated annealing at 2080 K for 10 ps). Additionally, electronic structure calculations indicate that it is a rare new material with triple Dirac cones, Dirac-like fermions, and node-loop features. Remarkably, this material is predicted to be a 2D phonon-mediated superconductor with T c = 4.7 K, higher than the boiling point of liquid helium (4.2 K). Surprisingly, the T c can be greatly enhanced up to 30 K by applying tensile strain at 12%. This is much higher than the temperature of liquid hydrogen (20.3 K). These outstanding properties may pave the way for potential applications of an AlB 6 -ptAl-array in nanoelectronics and nanomechanics. This work opens up a new branch of two-dimensional aluminum boride materials for exploration. The present study also opens a field of two-dimensional arrays of anti-van't Hoff/Le Bel motifs for study.
Bibliographical noteFunding Information:
B.-Y.S., Y.Z., H.-M.Y., and L.-M.Y. gratefully acknowledge support from the National Natural Science Foundation of China (21673087, 21873032) and a startup fund (2006013118 and 3004013105) and independent innovation research fund (0118013090) from Huazhong University of Science and Technology. J.H.L. gratefully acknowledges support from the Guangdong Natural Science Funds for Doctoral Program (Grant No. 2017A030310086). The authors thank the Minnesota Supercomputing Institute (MSI) at the University of Minnesota for supercomputing resources. This work is dedicated to Prof. Roald Hoffmann on the occasion of his 80th birthday.